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Wikipedia:WikiProject Conservatism

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    Welcome to WikiProject Conservatism! Whether you're a newcomer or regular, you'll receive encouragement and recognition for your achievements with conservatism-related articles. This project does not extol any point of view, political or otherwise, other than that of a neutral documentarian. Partly due to this, the project's scope has long become that of conservatism broadly construed, taking in a healthy periphery of (e.g., more academic) articles for contextualization.

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    • 06 Jan 2025 – Bepi Pezzulli (talk · edit · hist) was AfDed by Jan Arvid Götesson (t · c); see discussion (5 participants)
    • 30 Dec 2024 – Marion G. Wells Foundation (talk · edit · hist) AfDed by Doug Weller (t · c) was closed as redirect by Liz (t · c) on 06 Jan 2025; see discussion (4 participants)
    • 19 Dec 2024 – Progressive conservatism (talk · edit · hist) AfDed by Simonm223 (t · c) was closed as no consensus by Daniel (t · c) on 10 Jan 2025; see discussion (11 participants; relisted)

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    9 January 2025

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    7 January 2025

    6 January 2025

    For this watchlist but about 3X in length, visit: Wikipedia:WikiProject Conservatism/All recent changes
    WatchHot (Excerpt)
    A list of 10 related articles with the most (recent) edits total
    231 edits Zionism
    182 edits Jean-Marie Le Pen
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    104 edits Transgender health care misinformation
    67 edits Huddersfield sex abuse ring
    62 edits Anita Bryant
    56 edits Second presidential transition of Donald Trump
    53 edits Alice Weidel
    42 edits Mike Johnson
    42 edits JD Vance

    These are the articles that have been edited the most within the last seven days. Last updated 10 January 2025 by HotArticlesBot.


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    A list of 500 related articles with the most (recent) views total

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    List

    Period: 2024-12-01 to 2024-12-31

    Total views: 66,272,508

    Updated: 21:38, 5 January 2025 (UTC)

    Rank Page title Views Daily average Assessment Importance
    1 Donald Trump 1,570,414 50,658 B High
    2 Bing Crosby 662,848 21,382 B Low
    3 Ronald Reagan 610,942 19,707 FA Top
    4 Syrian opposition to Bashar al-Assad 598,973 19,321 C High
    5 Winston Churchill 501,206 16,167 GA Top
    6 Vladimir Putin 466,037 15,033 B High
    7 James Stewart 457,394 14,754 GA Low
    8 JD Vance 447,576 14,437 B Mid
    9 George W. Bush 442,954 14,288 B High
    10 Matt Gaetz 430,227 13,878 B Low
    11 George H. W. Bush 424,527 13,694 B High
    12 Nick Fuentes 409,336 13,204 B Low
    13 Gerald Ford 394,944 12,740 C High
    14 Sean Hannity 375,582 12,115 B Mid
    15 Richard Nixon 371,928 11,997 FA High
    16 People Power Party (South Korea) 367,468 11,853 C High
    17 2024 Magdeburg car attack 351,928 11,352 B Low
    18 Narendra Modi 344,887 11,125 GA Top
    19 Alternative for Germany 343,616 11,084 C Low
    20 Greg Gutfeld 343,594 11,083 C Low
    21 Theodore Roosevelt 332,355 10,721 B High
    22 Kay Granger 322,964 10,418 Start Mid
    23 Family of Donald Trump 311,230 10,039 B Low
    24 Mel Gibson 298,540 9,630 B Mid
    25 Benjamin Netanyahu 294,576 9,502 B Mid
    26 James Caan 292,898 9,448 C Low
    27 Dwight D. Eisenhower 281,975 9,095 B High
    28 Project 2025 262,041 8,452 B Mid
    29 Republican Party (United States) 245,959 7,934 B Top
    30 Woke 236,324 7,623 B Top
    31 Candace Owens 234,448 7,562 B Low
    32 French Revolution 234,332 7,559 B Top
    33 Jordan Peterson 232,419 7,497 C Low
    34 Department of Government Efficiency 223,623 7,213 B High
    35 Kemi Badenoch 222,504 7,177 B Low
    36 Lara Trump 216,395 6,980 C Low
    37 Joni Ernst 213,727 6,894 B Low
    38 Cold War 211,382 6,818 C Top
    39 Javier Milei 210,530 6,791 B Mid
    40 Atal Bihari Vajpayee 208,333 6,720 GA High
    41 Fyodor Dostoevsky 206,292 6,654 B Low
    42 Grover Cleveland 206,206 6,651 FA Mid
    43 Margaret Thatcher 202,394 6,528 A Top
    44 Bharatiya Janata Party 197,346 6,366 GA Top
    45 John Wayne 196,436 6,336 B Low
    46 Ron DeSantis 195,917 6,319 B Mid
    47 Rupert Murdoch 194,679 6,279 B Low
    48 Zionism 193,841 6,252 B Low
    49 Jon Voight 192,804 6,219 C Low
    50 Dick Cheney 192,520 6,210 C Mid
    51 Park Chung Hee 191,472 6,176 C Low
    52 Nancy Mace 191,005 6,161 B Low
    53 Alice Weidel 183,996 5,935 C Low
    54 Călin Georgescu 183,668 5,924 Start Unknown
    55 Laura Loomer 183,390 5,915 C Low
    56 Shirley Temple 181,924 5,868 B Low
    57 Mitch McConnell 180,001 5,806 B Mid
    58 Brett Cooper (commentator) 179,495 5,790 Start Low
    59 William McKinley 179,345 5,785 FA Low
    60 Denis Leary 177,805 5,735 C NA
    61 Marc Andreessen 176,436 5,691 C Mid
    62 Rishi Sunak 169,950 5,482 B High
    63 Linda McMahon 168,866 5,447 B Low
    64 Jacob Rees-Mogg 165,145 5,327 C Low
    65 Charles de Gaulle 163,798 5,283 B Mid
    66 Robert Duvall 163,129 5,262 B Low
    67 Tom Homan 161,041 5,194 Start Low
    68 Kelsey Grammer 160,483 5,176 B Low
    69 Taliban 159,312 5,139 B High
    70 Mitt Romney 159,225 5,136 FA High
    71 Herbert Hoover 156,614 5,052 B Mid
    72 Mike Johnson 155,727 5,023 C Mid
    73 Chuck Norris 155,427 5,013 B Low
    74 Shinzo Abe 153,574 4,954 B Mid
    75 Angela Merkel 153,336 4,946 GA High
    76 Nigel Farage 145,356 4,688 B Mid
    77 Muhammad Ali Jinnah 145,276 4,686 FA High
    78 Reform UK 145,055 4,679 C High
    79 Charlie Kirk 144,321 4,655 C Low
    80 Phil Robertson 144,240 4,652 C Low
    81 Neil Cavuto 143,460 4,627 Start Mid
    82 Boris Johnson 140,220 4,523 B High
    83 QAnon 140,120 4,520 GA Mid
    84 Clark Gable 137,307 4,429 B Low
    85 Falun Gong 136,967 4,418 B Mid
    86 Ben Shapiro 136,904 4,416 C Mid
    87 John Malkovich 135,672 4,376 C Low
    88 Recep Tayyip Erdoğan 135,277 4,363 B High
    89 Karoline Leavitt 131,082 4,228 Start Unknown
    90 Nayib Bukele 130,568 4,211 GA Low
    91 Chiang Kai-shek 128,149 4,133 C Low
    92 Warren G. Harding 125,757 4,056 FA Low
    93 Megyn Kelly 125,598 4,051 B Unknown
    94 Imran Khan 124,245 4,007 B Low
    95 1964 United States presidential election 123,471 3,982 C Mid
    96 Fianna Fáil 122,593 3,954 B Low
    97 Calvin Coolidge 121,888 3,931 FA High
    98 Ayn Rand 121,562 3,921 GA Mid
    99 Generation 119,231 3,846 B Mid
    100 Patrick Bet-David 119,114 3,842 C Low
    101 Anders Behring Breivik 118,823 3,833 C Low
    102 Pam Bondi 116,213 3,748 C Low
    103 Stephen Baldwin 114,658 3,698 B Low
    104 Liz Cheney 113,993 3,677 B High
    105 Liz Truss 113,723 3,668 FA Mid
    106 James A. Garfield 113,182 3,651 FA Low
    107 Deep state in the United States 111,201 3,587 Start Low
    108 William Howard Taft 110,281 3,557 FA Mid
    109 John McCain 109,818 3,542 FA Mid
    110 Francisco Franco 109,646 3,536 C Mid
    111 Patricia Heaton 108,044 3,485 C Low
    112 Taleb Al-Abdulmohsen 107,042 3,452 B Low
    113 Constitution of the United States 106,783 3,444 B High
    114 Marco Rubio 106,139 3,423 B Mid
    115 Harmeet Dhillon 105,211 3,393 Start Low
    116 Jeanine Pirro 104,291 3,364 B Low
    117 Charlton Heston 104,179 3,360 B Low
    118 James Woods 104,144 3,359 Start Low
    119 L. K. Advani 103,314 3,332 B High
    120 Barbara Stanwyck 102,920 3,320 B Low
    121 Conservative Party (UK) 99,140 3,198 B High
    122 Tucker Carlson 96,600 3,116 B High
    123 Nancy Reagan 96,430 3,110 B Mid
    124 Craig T. Nelson 95,542 3,082 Start Unknown
    125 Kari Lake 95,236 3,072 C Low
    126 Laura Ingraham 95,235 3,072 C Mid
    127 Charles Lindbergh 94,306 3,042 B Low
    128 Benjamin Harrison 94,191 3,038 FA Low
    129 Bo Derek 93,952 3,030 Start Low
    130 Rashtriya Swayamsevak Sangh 93,788 3,025 C Top
    131 Otto von Bismarck 93,279 3,009 B High
    132 Frank Capra 93,251 3,008 C Unknown
    133 Dmitry Medvedev 92,994 2,999 C High
    134 Steve Bannon 92,682 2,989 B Mid
    135 Deng Xiaoping 91,833 2,962 B Low
    136 Fine Gael 91,567 2,953 B High
    137 Neoliberalism 90,280 2,912 B Top
    138 Marjorie Taylor Greene 89,865 2,898 GA Low
    139 Rudy Giuliani 89,494 2,886 B Mid
    140 Kim Dae-jung 88,830 2,865 C Low
    141 Loretta Young 88,224 2,845 C Low
    142 Gary Sinise 87,131 2,810 C Low
    143 Groypers 86,546 2,791 B Low
    144 Ben Carson 85,987 2,773 C Low
    145 Condoleezza Rice 85,886 2,770 B Mid
    146 Chuck Grassley 85,499 2,758 C Mid
    147 Nicolas Sarkozy 85,484 2,757 B High
    148 Angie Harmon 85,286 2,751 C Low
    149 Tony Hinchcliffe 85,202 2,748 B Low
    150 Conservative Party of Canada 85,095 2,745 B High
    151 Mike Pence 85,089 2,744 B Mid
    152 Gary Cooper 84,996 2,741 FA Mid
    153 Stephen Harper 84,363 2,721 GA High
    154 Iran–Contra affair 84,356 2,721 GA Low
    155 Devin Nunes 84,192 2,715 C Low
    156 Stephen Miller (political advisor) 83,585 2,696 B Low
    157 Libertarianism 83,101 2,680 B High
    158 Melissa Joan Hart 80,872 2,608 B Low
    159 Spiro Agnew 80,799 2,606 FA Mid
    160 George Wallace 80,270 2,589 B Mid
    161 Arthur Wellesley, 1st Duke of Wellington 79,073 2,550 B Low
    162 Chuck Woolery 78,971 2,547 C Low
    163 Lee Myung-bak 78,619 2,536 C Mid
    164 Kristi Noem 78,597 2,535 B Low
    165 Fox News 78,547 2,533 C Mid
    166 Far-right politics 78,480 2,531 B Low
    167 Red states and blue states 77,854 2,511 C Mid
    168 Chester A. Arthur 77,153 2,488 FA Low
    169 Paul von Hindenburg 76,643 2,472 C Mid
    170 Thomas Sowell 76,406 2,464 C Mid
    171 Ted Cruz 75,670 2,440 B Mid
    172 Pat Sajak 75,377 2,431 C Low
    173 McCarthyism 75,069 2,421 C High
    174 Pat Fallon 74,856 2,414 Start Low
    175 Richard Grenell 74,807 2,413 C Low
    176 David Cameron 74,446 2,401 B Top
    177 Clarence Thomas 73,850 2,382 B Mid
    178 Neville Chamberlain 72,974 2,354 FA Mid
    179 Marine Le Pen 72,799 2,348 B Low
    180 Shigeru Ishiba 72,633 2,343 B Low
    181 David Duke 72,347 2,333 B Mid
    182 Kelly Loeffler 72,206 2,329 B Low
    183 George Santos 71,505 2,306 B Low
    184 Bob Hope 71,329 2,300 B Low
    185 Kayleigh McEnany 71,291 2,299 C Low
    186 John Locke 71,141 2,294 B Top
    187 Newt Gingrich 71,035 2,291 GA High
    188 Viktor Orbán 70,470 2,273 C Mid
    189 Dana Perino 70,389 2,270 C Low
    190 Alliance for the Union of Romanians 69,705 2,248 B Unknown
    191 Curtis Yarvin 68,939 2,223 C High
    192 Anna Paulina Luna 68,629 2,213 B Low
    193 Reagan (2024 film) 68,498 2,209 C Low
    194 Lindsey Graham 68,203 2,200 C Low
    195 Whig Party (United States) 68,083 2,196 C Low
    196 Doug Ford 67,951 2,191 B Low
    197 Karl Malone 67,263 2,169 Start Low
    198 Gadsden flag 66,834 2,155 B Low
    199 Dinesh D'Souza 66,565 2,147 B Mid
    200 Dave Mustaine 66,434 2,143 C Low
    201 Shiv Sena 66,409 2,142 C Unknown
    202 Lauren Boebert 65,982 2,128 B Low
    203 T. S. Eliot 65,729 2,120 B Low
    204 Kevin McCarthy 64,891 2,093 C Low
    205 Anthony Eden 64,805 2,090 B Mid
    206 Sarah Palin 64,344 2,075 C Mid
    207 Theresa May 64,221 2,071 B Mid
    208 Nikki Haley 64,152 2,069 B Low
    209 Dan Quayle 64,011 2,064 B Mid
    210 Milton Friedman 63,480 2,047 GA High
    211 Rutherford B. Hayes 63,371 2,044 FA Low
    212 Rachel Campos-Duffy 63,312 2,042 Start Low
    213 Snow White and the Evil Queen 63,035 2,033 C Unknown
    214 Hillbilly Elegy 62,835 2,026 B Low
    215 Dan Crenshaw 62,523 2,016 B Low
    216 John Major 62,311 2,010 B High
    217 Vinayak Damodar Savarkar 62,017 2,000 B High
    218 Second presidency of Donald Trump 61,794 1,993 C Low
    219 National Rally 61,339 1,978 GA High
    220 Donald Rumsfeld 61,313 1,977 B Mid
    221 Liberty Korea Party 61,302 1,977 Start High
    222 Alessandra Mussolini 61,284 1,976 B Low
    223 John Roberts 61,097 1,970 B High
    224 Kellyanne Conway 60,981 1,967 B Low
    225 The Daily Wire 60,691 1,957 C Low
    226 Mark Rutte 60,663 1,956 C High
    227 John Thune 60,498 1,951 C Low
    228 Robert Davi 60,364 1,947 Start Low
    229 Manosphere 60,315 1,945 C Low
    230 Right-wing politics 60,174 1,941 C Top
    231 John Kennedy (Louisiana politician) 60,026 1,936 C Low
    232 House of Bourbon 60,011 1,935 B High
    233 The Republicans (France) 59,758 1,927 Start Low
    234 Political appointments of the second Trump administration 59,747 1,927 List Low
    235 Michael Waltz 59,465 1,918 Start Low
    236 Ustaše 59,450 1,917 C High
    237 Ginger Rogers 59,264 1,911 C Unknown
    238 David Perdue 59,263 1,911 B Low
    239 Matt Walsh (political commentator) 59,034 1,904 C Low
    240 Roger Stone 58,984 1,902 C Low
    241 Capitalism 58,852 1,898 C Top
    242 Saagar Enjeti 58,205 1,877 Stub Unknown
    243 Victoria Spartz 58,096 1,874 C Low
    244 Christian Democratic Union of Germany 58,039 1,872 C High
    245 Stacey Dash 57,905 1,867 C Low
    246 Conservatism 57,879 1,867 B Top
    247 Strom Thurmond 57,390 1,851 B Mid
    248 Rand Paul 57,372 1,850 GA Mid
    249 Last Man Standing (American TV series) 57,053 1,840 B Low
    250 Rush Limbaugh 56,373 1,818 B High
    251 Mullah Omar 56,053 1,808 B High
    252 Barry Goldwater 55,885 1,802 B High
    253 Truth Social 55,867 1,802 B Low
    254 Silencer (firearms) 55,671 1,795 C Low
    255 Bob Dole 55,262 1,782 B Low
    256 W. B. Yeats 55,103 1,777 FA Low
    257 Proud Boys 54,713 1,764 C Low
    258 Greg Abbott 54,692 1,764 B Mid
    259 1924 United States presidential election 54,330 1,752 C Low
    260 Right-wing populism 54,139 1,746 B High
    261 Tom Clancy 54,126 1,746 C Low
    262 Make America Great Again 53,977 1,741 B Low
    263 Edward Teller 53,939 1,739 FA Low
    264 Chris Christie 53,745 1,733 C Low
    265 GypsyCrusader 53,258 1,718 C Low
    266 Ron Paul 52,687 1,699 C Mid
    267 Second presidential transition of Donald Trump 52,651 1,698 Start Low
    268 Daily Mail 52,581 1,696 B Mid
    269 Left–right political spectrum 52,572 1,695 C Top
    270 Billy Long 52,398 1,690 Start Low
    271 Great Replacement conspiracy theory 52,390 1,690 C Top
    272 Trumpism 52,238 1,685 B Mid
    273 Menachem Begin 52,230 1,684 B Mid
    274 Fred Thompson 51,866 1,673 B Low
    275 Cromwell family 51,673 1,666 List Low
    276 Dennis Prager 51,332 1,655 C Low
    277 White supremacy 51,154 1,650 B Low
    278 Party of Young People 51,059 1,647 Unknown Unknown
    279 Free Democratic Party (Germany) 50,908 1,642 C Mid
    280 Jair Bolsonaro 50,562 1,631 B Mid
    281 Aontú 50,311 1,622 C Low
    282 Ann Coulter 50,090 1,615 B Mid
    283 Aleksandr Dugin 50,000 1,612 C Mid
    284 Brian Mast 49,638 1,601 C Low
    285 Benjamin Disraeli 49,594 1,599 FA Top
    286 Bill O'Reilly (political commentator) 49,342 1,591 B Mid
    287 United Russia 49,332 1,591 B High
    288 Oliver North 49,290 1,590 C Mid
    289 Alpha and beta male 49,116 1,584 C Low
    290 Paul Ryan 49,004 1,580 C Mid
    291 Jeb Bush 48,985 1,580 B Low
    292 Neoconservatism 48,591 1,567 C Top
    293 12 Rules for Life 48,556 1,566 B Mid
    294 Rumble (company) 48,237 1,556 Start Low
    295 Laura Bush 48,179 1,554 GA Low
    296 Ward Bond 48,157 1,553 C Low
    297 Adam Kinzinger 48,149 1,553 C Low
    298 Islamism 48,085 1,551 B High
    299 Chip Roy 48,080 1,550 C Low
    300 Don King 47,978 1,547 B Low
    301 The Times of India 47,786 1,541 C Mid
    302 Dave Ramsey 47,706 1,538 C Unknown
    303 Elise Stefanik 47,665 1,537 B Low
    304 Aleksandr Solzhenitsyn 47,345 1,527 B Mid
    305 Brothers of Italy 47,133 1,520 B Mid
    306 Éamon de Valera 47,085 1,518 B High
    307 Ted Nugent 47,083 1,518 C Low
    308 Lil Pump 46,742 1,507 B Low
    309 Donald Trump 2024 presidential campaign 46,566 1,502 B Low
    310 CDU/CSU 46,544 1,501 C Low
    311 The Epoch Times 46,489 1,499 B Low
    312 Tommy Tuberville 46,473 1,499 B Low
    313 Rick Scott 46,248 1,491 C Low
    314 Deus vult 46,141 1,488 Start Low
    315 John C. Calhoun 45,810 1,477 FA Top
    316 Jack Kemp 45,573 1,470 GA Mid
    317 Harold Macmillan 45,316 1,461 B High
    318 Fred MacMurray 45,087 1,454 C Low
    319 Mahathir Mohamad 44,963 1,450 GA High
    320 Jane Russell 44,839 1,446 B Low
    321 John Rocker 44,662 1,440 C Unknown
    322 Shiv Sena (UBT) 44,436 1,433 C Mid
    323 Scott Baio 44,178 1,425 Start Low
    324 Mary Matalin 44,081 1,421 C Low
    325 Riley Gaines 44,057 1,421 B Mid
    326 Tradwife 44,005 1,419 B Low
    327 Jacobitism 43,993 1,419 B High
    328 Sebastian Gorka 43,840 1,414 C Unknown
    329 Joe Scarborough 43,812 1,413 B Low
    330 Douglas Murray (author) 43,690 1,409 C Low
    331 Liberal Democratic Party (Japan) 43,582 1,405 C High
    332 James Cagney 43,338 1,398 B Low
    333 S.O.S. Romania 43,320 1,397 B Unknown
    334 Thomas Massie 43,221 1,394 B Low
    335 Michael Farmer, Baron Farmer 43,180 1,392 C Low
    336 Turning Point USA 43,144 1,391 C Low
    337 Dan Bongino 43,101 1,390 C Mid
    338 Trump derangement syndrome 42,993 1,386 C Mid
    339 Ray Bradbury 42,911 1,384 B Low
    340 Jackson Hinkle 42,889 1,383 B Low
    341 Critical race theory 42,870 1,382 C Low
    342 Brian Mulroney 42,803 1,380 B High
    343 Amy Coney Barrett 42,154 1,359 C Low
    344 Pat Buchanan 41,839 1,349 B Mid
    345 The Wall Street Journal 41,734 1,346 B Mid
    346 Likud 41,688 1,344 C Low
    347 The Daily Telegraph 41,339 1,333 C Low
    348 Booker T. Washington 41,239 1,330 B Low
    349 Trey Gowdy 41,223 1,329 C Mid
    350 Brett Kavanaugh 41,130 1,326 B High
    351 Dave Rubin 40,977 1,321 C Low
    352 Federalist Party 40,881 1,318 C Low
    353 Pat Boone 40,881 1,318 C Low
    354 First presidency of Donald Trump 40,691 1,312 B Low
    355 Antonin Scalia 40,073 1,292 FA High
    356 David Mamet 39,796 1,283 C Low
    357 Martin Heidegger 39,679 1,279 C Low
    358 Monica Crowley 39,656 1,279 C Low
    359 Iron Guard 39,628 1,278 C Mid
    360 Infowars 39,577 1,276 C Low
    361 Laissez-faire 39,540 1,275 C Top
    362 Milo Yiannopoulos 39,316 1,268 C Low
    363 Dark Enlightenment 39,240 1,265 Start Mid
    364 Elaine Chao 39,215 1,265 B Low
    365 Ayaan Hirsi Ali 39,190 1,264 B Low
    366 Hallmark Channel 38,957 1,256 B Low
    367 False or misleading statements by Donald Trump 38,929 1,255 B Low
    368 John Bolton 38,895 1,254 C Mid
    369 Edward Heath 38,801 1,251 B High
    370 William F. Buckley Jr. 38,712 1,248 B Top
    371 Geert Wilders 38,598 1,245 B Low
    372 Views of Elon Musk 38,184 1,231 B Mid
    373 Hillsdale College 38,156 1,230 C Low
    374 Lisa Murkowski 38,094 1,228 C High
    375 First impeachment of Donald Trump 38,038 1,227 B High
    376 John Layfield 37,641 1,214 B Low
    377 The Fountainhead 37,378 1,205 FA Low
    378 Susie Wiles 37,366 1,205 C Low
    379 D. H. Lawrence 37,341 1,204 B Unknown
    380 Islamophobia 37,298 1,203 C Mid
    381 Louis B. Mayer 37,182 1,199 C Low
    382 Tomi Lahren 36,898 1,190 Start Low
    383 Leo Varadkar 36,842 1,188 B Low
    384 Roger Ailes 36,792 1,186 C Mid
    385 Jack Posobiec 36,706 1,184 B Low
    386 Tim Montgomerie 36,211 1,168 C Mid
    387 Harold Holt 36,092 1,164 B Mid
    388 Thomas Mann 36,058 1,163 C Mid
    389 Terri Schiavo case 35,982 1,160 GA Low
    390 Jeanette Nuñez 35,973 1,160 C Unknown
    391 Mike Huckabee 35,953 1,159 B Mid
    392 Samuel Alito 35,867 1,157 C Mid
    393 Justice and Development Party (Turkey) 35,855 1,156 B Low
    394 Gretchen Carlson 35,761 1,153 B Low
    395 Franklin Graham 35,482 1,144 B Low
    396 William Barr 35,437 1,143 B Unknown
    397 The Heritage Foundation 35,426 1,142 B High
    398 Original sin 35,308 1,138 C Low
    399 Liberty University 35,291 1,138 B Mid
    400 Bourbon Restoration in France 35,255 1,137 C High
    401 Charles Hurt 35,232 1,136 Stub Unknown
    402 Helmut Kohl 35,098 1,132 B High
    403 Kalergi Plan 35,050 1,130 Start Mid
    404 Edmund Burke 34,932 1,126 B Top
    405 Anthony Scaramucci 34,873 1,124 C Low
    406 Walter Brennan 34,755 1,121 C Low
    407 John Ratcliffe (American politician) 34,681 1,118 C Low
    408 Reform Party of the United States of America 34,601 1,116 C Low
    409 Callista Gingrich 34,533 1,113 C Low
    410 John Birch Society 34,416 1,110 C Low
    411 The Gateway Pundit 34,255 1,105 C Unknown
    412 Sarah Huckabee Sanders 34,243 1,104 C Low
    413 Tea Party movement 33,837 1,091 C Mid
    414 Nawaz Sharif 33,818 1,090 B Unknown
    415 Moshe Dayan 33,708 1,087 B Mid
    416 Glenn Beck 33,703 1,087 B Mid
    417 Classical liberalism 33,668 1,086 B Top
    418 Alt-right 33,563 1,082 C Mid
    419 New York Post 33,558 1,082 C Low
    420 Zia-ul-Haq 33,518 1,081 B High
    421 Breitbart News 33,503 1,080 C Mid
    422 Peggy Noonan 33,480 1,080 C Low
    423 Freedom Caucus 33,115 1,068 C Low
    424 Patriots for Europe 33,080 1,067 C Low
    425 Conservatism in the United States 32,998 1,064 B Top
    426 Chloe Cole 32,970 1,063 C Low
    427 Anarcho-capitalism 32,898 1,061 B Low
    428 Michael Reagan 32,823 1,058 C Low
    429 Madison Cawthorn 32,769 1,057 C Low
    430 Tom Cotton 32,687 1,054 C Low
    431 Jemima Goldsmith 32,470 1,047 C Unknown
    432 Curtis Sliwa 32,376 1,044 C Unknown
    433 People's Party of Canada 32,317 1,042 C Low
    434 Honoré de Balzac 32,114 1,035 FA High
    435 Liaquat Ali Khan 32,076 1,034 B Low
    436 UK Independence Party 32,066 1,034 B Low
    437 Christopher Luxon 31,928 1,029 B Unknown
    438 Progressivism 31,707 1,022 C Mid
    439 Lee Hsien Loong 31,388 1,012 C Mid
    440 Frank Bruno 31,322 1,010 Start Unknown
    441 Craig James (running back) 31,293 1,009 C Low
    442 Morgan Ortagus 31,226 1,007 C Unknown
    443 Friedrich Hayek 31,225 1,007 B Top
    444 Shiromani Akali Dal 31,115 1,003 C High
    445 Victor Davis Hanson 31,059 1,001 B Mid
    446 Joe Arpaio 31,033 1,001 B Low
    447 Dennis Miller 30,920 997 Start Low
    448 Trump wall 30,918 997 Redirect NA
    449 2024 United Kingdom riots 30,898 996 B Low
    450 Enoch Powell 30,876 996 C High
    451 Lord Randolph Churchill 30,810 993 C Unknown
    452 Yun Po-sun 30,724 991 Start Low
    453 Neil Gorsuch 30,722 991 B Mid
    454 Patriarchy 30,518 984 B Low
    455 António de Oliveira Salazar 30,508 984 B Unknown
    456 Charles Saatchi 30,205 974 C Low
    457 Naomi Seibt 30,169 973 Start Low
    458 Primogeniture 30,141 972 Start Low
    459 Austrian school of economics 30,105 971 B Mid
    460 Franz von Papen 30,088 970 B Low
    461 Marsha Blackburn 30,071 970 C Low
    462 Jean-Marie Le Pen 29,904 964 B Mid
    463 Christian nationalism 29,844 962 Start High
    464 Grey Wolves (organization) 29,737 959 B Mid
    465 Richard B. Spencer 29,611 955 C Low
    466 Mike Lindell 29,578 954 C Low
    467 Bible Belt 29,563 953 C Low
    468 Helen Hayes 29,524 952 B Low
    469 Gavin McInnes 29,493 951 C Low
    470 Bret Stephens 29,485 951 C Low
    471 Am I Racist? 29,454 950 Start Mid
    472 John O'Hurley 29,268 944 Start Low
    473 Brooke Rollins 29,233 943 Start Low
    474 Nuclear family 29,120 939 Start Low
    475 Agenda 47 28,959 934 C Top
    476 La Libertad Avanza 28,950 933 C Low
    477 Johnny Ramone 28,790 928 C Low
    478 Chris Williamson (TV personality) 28,766 927 Stub Low
    479 Flannery O'Connor 28,721 926 A Low
    480 Law and Justice 28,623 923 C High
    481 Social stratification 28,519 919 C High
    482 Danielle Smith 28,469 918 B Unknown
    483 Edward Wood, 1st Earl of Halifax 28,445 917 C Low
    484 Christmas traditions 28,358 914 C Mid
    485 Irene Dunne 28,354 914 GA Low
    486 Tammy Bruce 28,248 911 Start Low
    487 Koch family 28,079 905 Start High
    488 White movement 28,061 905 B Mid
    489 Societal collapse 28,044 904 Start High
    490 Mark Levin 27,946 901 B High
    491 Redneck 27,839 898 C Low
    492 Hindutva 27,727 894 B Top
    493 Twitter under Elon Musk 27,667 892 B Mid
    494 2016 Republican Party presidential primaries 27,646 891 B Mid
    495 Samuel Taylor Coleridge 27,625 891 C Top
    496 Anti-communism 27,505 887 B Mid
    497 Will Cain 27,379 883 Start Mid
    498 Alec Douglas-Home 27,376 883 FA Low
    499 Steele dossier 27,306 880 B Low
    500 Ben Stein 27,278 879 C Low

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    New articles

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    Rules | Match log | Results page (for watching) | Last updated: 2025-01-09 20:35 (UTC)

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    In The Signpost

    One of various articles to this effect
    July 2018
    DISCUSSION REPORT
    WikiProject Conservatism Comes Under Fire

    By Lionelt

    WikiProject Conservatism was a topic of discussion at the Administrators' Noticeboard/Incident (AN/I). Objective3000 started a thread where he expressed concern regarding the number of RFC notices posted on the Discussion page suggesting that such notices "could result in swaying consensus by selective notification." Several editors participated in the relatively abbreviated six hour discussion. The assertion that the project is a "club for conservatives" was countered by editors listing examples of users who "profess no political persuasion." It was also noted that notification of WikiProjects regarding ongoing discussions is explicitly permitted by the WP:Canvassing guideline.

    At one point the discussion segued to feedback about The Right Stuff. Member SPECIFICO wrote: "One thing I enjoy about the Conservatism Project is the handy newsletter that members receive on our talk pages." Atsme praised the newsletter as "first-class entertainment...BIGLY...first-class...nothing even comes close...it's amazing." Some good-natured sarcasm was offered with Objective3000 observing, "Well, they got the color right" and MrX's followup, "Wow. Yellow is the new red."

    Admin Oshwah closed the thread with the result "definitely not an issue for ANI" and directing editors to the project Discussion page for any further discussion. Editor's note: originally the design and color of The Right Stuff was chosen to mimic an old, paper newspaper.

    Add the Project Discussion page to your watchlist for the "latest RFCs" at WikiProject Conservatism Watch (Discuss this story)
    ARTICLES REPORT
    Margaret Thatcher Makes History Again

    By Lionelt

    Margaret Thatcher is the first article promoted at the new WikiProject Conservatism A-Class review. Congratulations to Neveselbert. A-Class is a quality rating which is ranked higher than GA (Good article) but the criteria are not as rigorous as FA (Featued article). WikiProject Conservatism is one of only two WikiProjects offering A-Class review, the other being WikiProject Military History. Nominate your article here. (Discuss this story)
    RECENT RESEARCH
    Research About AN/I

    By Lionelt

    Reprinted in part from the April 26, 2018 issue of The Signpost; written by Zarasophos

    Out of over one hundred questioned editors, only twenty-seven (27%) are happy with the way reports of conflicts between editors are handled on the Administrators' Incident Noticeboard (AN/I), according to a recent survey . The survey also found that dissatisfaction has varied reasons including "defensive cliques" and biased administrators as well as fear of a "boomerang effect" due to a lacking rule for scope on AN/I reports. The survey also included an analysis of available quantitative data about AN/I. Some notable takeaways:

    • 53% avoided making a report due to fearing it would not be handled appropriately
    • "Otherwise 'popular' users often avoid heavy sanctions for issues that would get new editors banned."
    • "Discussions need to be clerked to keep them from raising more problems than they solve."

    In the wake of Zarasophos' article editors discussed the AN/I survey at The Signpost and also at AN/I. Ironically a portion of the AN/I thread was hatted due to "off-topic sniping." To follow-up the problems identified by the research project the Wikimedia Foundation Anti-Harassment Tools team and Support and Safety team initiated a discussion. You can express your thoughts and ideas here.

    (Discuss this story)

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    Is Wikipedia Politically Biased? Perhaps

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    By Tilman Bayer


     

    A monthly overview of recent academic research about Wikipedia and other Wikimedia projects, also published as the Wikimedia Research Newsletter.


    Report by conservative think-tank presents ample quantitative evidence for "mild to moderate" "left-leaning bias" on Wikipedia

    A paper titled "Is Wikipedia Politically Biased?"[1] answers that question with a qualified yes:

    [...] this report measures the sentiment and emotion with which political terms are used in [English] Wikipedia articles, finding that Wikipedia entries are more likely to attach negative sentiment to terms associated with a right-leaning political orientation than to left-leaning terms. Moreover, terms that suggest a right-wing political stance are more frequently connected with emotions of anger and disgust than those that suggest a left-wing stance. Conversely, terms associated with left-leaning ideology are more frequently linked with the emotion of joy than are right-leaning terms.
    Our findings suggest that Wikipedia is not entirely living up to its neutral point of view policy, which aims to ensure that content is presented in an unbiased and balanced manner.

    The author (David Rozado, an associate professor at Otago Polytechnic) has published ample peer-reviewed research on related matters before, some of which was featured e.g. in The Guardian and The New York Times. In contrast, the present report is not peer-reviewed and was not posted in an academic venue, unlike most research we cover here usually. Rather, it was published (and possibly commissioned) by the Manhattan Institute, a conservative US think tank, which presumably found its results not too objectionable. (Also, some – broken – URLs in the PDF suggest that Manhattan Institute staff members were involved in the writing of the paper.) Still, the report indicates an effort to adhere to various standards of academic research publications, including some fairly detailed descriptions of the methods and data used. It is worth taking it more seriously than, for example, another recent report that alleged a different form of political bias on Wikipedia, which had likewise been commissioned by an advocacy organization and authored by an academic researcher, but was met with severe criticism by the Wikimedia Foundation (who called it out for "unsubstantiated claims of bias") and volunteer editors (see prior Signpost coverage).

    That isn't to say that there can't be some questions about the validity of Rozado's results, and in particular about how to interpret them. But let's first go through the paper's methods and data sources in more detail.

    Determining the sentiment and emotion in Wikipedia's coverage

    The report's main results regarding Wikipedia are obtained as follows:

    "We first gather a set of target terms (N=1,628) with political connotations (e.g., names of recent U.S. presidents, U.S. congressmembers, U.S. Supreme Court justices, or prime ministers of Western countries) from external sources. We then identify all mentions in English-language Wikipedia articles of those terms.

    We then extract the paragraphs in which those terms occur to provide the context in which the target terms are used and feed a random sample of those text snippets to an LLM (OpenAI’s gpt-3.5-turbo), which annotates the sentiment/emotion with which the target term is used in the snippet. To our knowledge, this is the first analysis of political bias in Wikipedia content using modern LLMs for annotation of sentiment/emotion."

    The sentiment classification rates the mention of a terms as negative, neutral or positive. (For the purpose of forming averages this is converted into a quantitative scale from -1 to +1.) See the end of this review for some concrete examples from the paper's published dataset.

    The emotion classification uses "Ekman’s six basic emotions (anger, disgust, fear, joy, sadness, and surprise) plus neutral."

    The annotation method used appears to be an effort to avoid the shortcomings of popular existing sentiment analysis techniques, which often only rate the overall emotional stance of a given text overall without determining whether it actually applies to a specific entity mentioned in it (or in some cases even fail to handle negations, e.g. by classifying "I am not happy" as a positive emotion). Rozado justifies the "decision to use automated annotation" (which presumably rendered considerable cost savings, also by resorting to OpenAI's older GPT 3.5 model rather than the more powerful but more expensive GPT-4 API released in March 2023) citing "recent evidence showing how top-of-the-rank LLMs outperform crowd workers for text-annotation tasks such as stance detection." This is indeed becoming a more widely used choice for text classification. But Rozado appears to have skipped the usual step of evaluating the accuracy of this automated method (and possibly improving the prompts it used) against a gold standard sample from (human) expert raters.

    Selecting topics to examine for bias

    As for the selection of terms whose Wikipedia coverage to annotate with this classifier, Rozado does a lot of due diligence to avoid cherry-picking: "To reduce the degrees of freedom of our analysis, we mostly use external sources of terms [including Wikipedia itself, e.g. its list of members of the 11th US Congress] to conceptualize a political category into left- and right-leaning terms, as well as to choose the set of terms to include in each category." This addresses an important source of researcher bias.

    Overall, the study arrives at 12 different groups of such terms:

    • 8 of these refer to people (e.g. US presidents, US senators, UK members of parliament, US journalists).
    • Two are about organizations (US think tanks and media organizations).
    • The other two groups contain "Terms that describe political orientation", i.e. expressions that carry a left-leaning or right-leaning meaning themselves:
      • 18 "political leanings" (where "Rightists" receives the lowest average sentiment and "Left winger" the highest), and
      • 21 "extreme political ideologies" (where "Ultraconservative" scores lowest and "radical-left" has the highest – but still slightly negative – average sentiment)

    What is "left-leaning" and "right-leaning"?

    As discussed, Rozado's methods for generating these lists of people and organizations seem reasonably transparent and objective. It gets a bit murkier when it comes to splitting them into "left-leaning" and "right-leaning", where the chosen methods remain unclear and/or questionable in some cases. Of course there is a natural choice available for US Congress members, where the confines of the US two-party system mean that the left-right spectrum can be easily mapped easily to Democrats vs. Republicans (disregarding a small number of independents or libertarians).

    In other cases, Rozado was able to use external data about political leanings, e.g. "a list of politically aligned U.S.-based journalists" from Politico. There may be questions about construct validity here (e.g. it classifies Glenn Greenwald or Andrew Sullivan as "journalists with the left"), but at least this data is transparent and determined by a source not invested in the present paper's findings.

    But for example the list of UK MPs used contains politicians from 14 different parties (plus independents). Even if one were to confine the left vs. right labels to the two largest groups in the UK House of Commons (Tories vs. Labour and Co-operative Party, which appears to have been the author's choice judging from Figure 5), the presence of a substantial number of parliamentarians from other parties to the left or right of those would make the validity of this binary score more questionable than in the US case. Rozado appears to acknowledge a related potential issue in a side remark when trying to offer an explanation for one of the paper's negative results (no bias) in this case: "The disparity of sentiment associations in Wikipedia articles between U.S. Congressmembers and U.K. MPs based on their political affiliation may be due in part to the higher level of polarization in the U.S. compared to the U.K."

     
    Most negative sentiment among Western leaders: Former Australian PM Tony Abbott
     
    Most positive sentiment among Western leaders: Former Australian PM Scott Morrison

    This kind of question become even more complicated for the "Leaders of Western Countries" list (where Tony Abbott scored the most negative average sentiment, and José Luis Rodríguez Zapatero and Scott Morrison appear to be in a tie for the most positive average sentiment). Most of these countries do not have a two-party system either. Sure, their leaders usually (like in the UK case) hail from one of the two largest parties, one of which is more to the left and the another more to the right. But it certainly seems to matter for the purpose of Rozado's research question whether that major party is more moderate (center-left or center-right, with other parties between it and the far left or far right) or more radical (i.e. extending all the way to the far-left or far-right spectrum of elected politicians).

    What's more, the analysis for this last group compares political orientations across multiple countries. Which brings us to a problem that Wikipedia's Jimmy Wales had already pointed to back in 2006 in response a conservative US blogger who had argued that there was "a liberal bias in many hot-button topic entries" on English Wikipedia:

    "The Wikipedia community is very diverse, from liberal to conservative to libertarian and beyond. If averages mattered, and due to the nature of the wiki software (no voting) they almost certainly don't, I would say that the Wikipedia community is slightly more liberal than the U.S. population on average, because we are global and the international community of English speakers is slightly more liberal than the U.S. population. ... The idea that neutrality can only be achieved if we have some exact demographic matchup to [the] United States of America is preposterous."

    We already discussed this issue in our earlier reviews of a notable series of papers by Greenstein and Zhu (see e.g.: "Language analysis finds Wikipedia's political bias moving from left to right", 2012), which had relied on a US-centric method of defining left-leaning and right-leaning (namely, a corpus derived from the US Congressional Record). Those studies form a large part of what Rozado cites as "[a] substantial body of literature [that]—albeit with some exceptions—has highlighted a perceived bias in Wikipedia content in favor of left-leaning perspectives." (The cited exception is a paper[2] that had found "a small to medium size coverage bias against [members of parliament] from the center-left parties in Germany and in France", and identified patterns of "partisan contributions" as a plausible cause.)

    Similarly, 8 out of the 10 groups of people and organizations analyzed in Rozado's study are from the US (the two exceptions being the aforementioned lists of UK MPs and leaders of Western countries).

    In other words, one potential reason for the disparities found by Rozado might simply be that he is measuring an international encyclopedia with a (largely) national yardstick of fairness. This shouldn't let us dismiss his findings too easily. But it is a bit disappointing that this possibility is nowhere addressed in the paper, even though Rozado diligently discusses some other potential limitations of the results. E.g. he notes that "some research has suggested that conservatives themselves are more prone to negative emotions and more sensitive to threats than liberals", but points out that the general validity of those research results remains doubtful.

    Another limitation is that a simple binary left vs. right classification might be hiding factors that can shed further light on bias findings. Even in the US with its two-party system, political scientists and analysts have long moved to less simplistic measures of political orientations. A widely used one is the NOMINATE method which assigns members of the US Congress continuous scores based on their detailed voting record, one of which corresponds to the left-right spectrum as traditionally understood. One finding based on that measure that seems relevant in context of the present study is the (widely discussed but itself controversial) asymmetric polarization thesis, which argues that "Polarization among U.S. legislators is asymmetric, as it has primarily been driven by a substantial rightward shift among congressional Republicans since the 1970s, alongside a much smaller leftward shift among congressional Democrats" (as summarized in the linked Wikipedia article). If, for example, higher polarization was associated with negative sentiments, this could be a potential explanation for Rozado's results. Again, this has to remain speculative, but it seems another notable omission in the paper's discussion of limitations.

    What does "bias" mean here?

    A fundamental problem of this study, which, to be fair, it shares with much fairness and bias research (in particular on Wikipedia's gender gap, where many studies similarly focus on binary comparisons that are likely to successfully appeal to an intuitive sense of fairness) consists of justifying its answers to the following two basic questions:

    1. What would be a perfectly fair baseline, a result that makes us confident to call Wikipedia unbiased?
    2. If there are deviations from that baseline (often labeled disparities, gaps or biases), what are the reasons for that – can we confidently assume they were caused by Wikipedia itself (e.g. demographic imbalances in Wikipedia's editorship), or are they more plausibly attributed to external factors?

    Regarding 1 (defining a baseline of unbiasedness), Rozado simply assumes that this should imply statistically indistinguishable levels of average sentiment between left and right-leaning terms. However, as cautioned by one leading scholar on quantitative measures of bias, "the 'one true fairness definition' is a wild goose chase" – there are often multiple different definitions available that can all be justified on ethical grounds, and are often contradictory. Above, we already alluded to two potentially diverging notions of political unbiasedness for Wikipedia (using an international instead of US metric for left vs right leaning, and taking into account polarization levels for politicians).

    But yet another question, highly relevant for Wikipedians interested in addressing the potential problems reported in this paper, is how much its definition lines up with Wikipedia's own definition of neutrality. Rozado clearly thinks that it does:

    Wikipedia’s neutral point of view (NPOV) policy aims for articles in Wikipedia to be written in an impartial and unbiased tone. Our results suggest that Wikipedia’s NPOV policy is not achieving its stated goal of political-viewpoint neutrality in Wikipedia articles.

    WP:NPOV indeed calls for avoiding subjective language and expressing judgments and opinions in Wikipedia's own voice, and Rozado's findings about the presence of non-neutral sentiments and emotions in Wikipedia articles are of some concern in that regard. However, that is not the core definition of NPOV. Rather, it refers to "representing fairly, proportionately, and, as far as possible, without editorial bias, all the significant views that have been published by reliable sources on a topic." What if the coverage of the terms examined by Rozado (politicians, etc.) in those reliable sources, in their aggregate, were also biased in the sense of Rozado's definition? US progressives might be inclined to invoke the snarky dictum "reality has a liberal bias" by comedian Stephen Colbert. Of course, conservatives might object that Wikipedia's definition of reliable sources (having "a reputation for fact-checking and accuracy") is itself biased, or applied in a biased way by Wikipedians. For some of these conservatives (at least those that are not also conservative feminists) it may be instructive to compare examinations of Wikipedia's gender gaps, which frequently focus on specific groups of notable people like in Rozado's study. And like him, they often implicitly assume a baseline of unbiasedness that implies perfect symmetry in Wikipedia's coverage – i.e. the absence of gaps or disparities. Wikipedians often object that this is in tension with the aforementioned requirement to reflect coverage in reliable sources. For example, Wikipedia's list of Fields medalists (the "Nobel prize of Mathematics") is 97% male – not because of Wikipedia editors' biases against women, but because of a severe gender imbalance in the field of mathematics that is only changing slowly, i.e. factors outside Wikipedia's influence.

    All this brings us to question 2. above (causality). While Rozado uses carefully couched language in this regard ("suggests" etc, e.g. "These trends constitute suggestive evidence of political bias embedded in Wikipedia articles"), such qualifications are unsurprisingly absent in much of the media coverage of this study (see also this issue's In the media). For example, the conservative magazine The American Spectator titled its article about the paper "Now We've Got Proof that Wikipedia is Biased."

    Commendably, the paper is accompanied by a published dataset, consisting of the analyzed Wikipedia text snippets together with the mentioned term and the sentiment or emotion identified by the automated annotation. For illustration, below are the sentiment ratings for mentions of the Yankee Institute for Public Policy (the last term in the dataset, as a non-cherry-picked example), with the term bolded:

    Dataset excerpt: Wikipedia paragraphs with sentiment for "Yankee Institute for Public Policy"
    positive "Carol Platt Liebau is president of the Yankee Institute for Public Policy.Liebau named new president of Yankee Institute She is also an attorney, political analyst, and conservative commentator. Her book Prude: How the Sex-Obsessed Culture Damages Girls (and America, Too!) was published in 2007."
    neutral "Affiliates

    Regular members are described as ""full-service think tanks"" operating independently within their respective states.

    Alabama: Alabama Policy Institute
    Alaska: Alaska Policy Forum
    [...]
    Connecticut: Yankee Institute for Public Policy
    [...]
    Wisconsin: MacIver Institute for Public Policy, Badger Institute, Wisconsin Institute for Law and Liberty, Institute for Reforming Government
    Wyoming: Wyoming Liberty Group"
    positive "The Yankee Institute for Public Policy is a free market, limited government American think tank based in Hartford, Connecticut, that researches Connecticut public policy questions. Organized as a 501(c)(3), the group's stated mission is to ""develop and advocate for free market, limited government public policy solutions in Connecticut."" Yankee was founded in 1984 by Bernard Zimmern, a French entrepreneur who was living in Norwalk, Connecticut, and Professor Gerald Gunderson of Trinity College. The organization is a member of the State Policy Network."
    neutral "He is formerly Chairman of the Yankee Institute for Public Policy. On November 3, 2015, he was elected First Selectman in his hometown of Stonington, Connecticut, which he once represented in Congress. He defeated the incumbent, George Crouse. Simmons did not seek reelection in 2019."
    negative "In Connecticut the union is closely identified with liberal Democratic politicians such as Governor Dannel Malloy and has clashed frequently with fiscally conservative Republicans such as former Governor John G. Rowland as well as the Yankee Institute for Public Policy, a free-market think tank."
    positive "In 2021, after leaving elective office, she was named a Board Director of several organizations. One is the Center for Workforce Inclusion, a national nonprofit in Washington, DC, that works to provide meaningful employment opportunities for older individuals. Another is the William F. Buckley Program at Yale, which aims to promote intellectual diversity, expand political discourse on campus, and expose students to often-unvoiced views at Yale University. She also serves on the Board of the Helicon Foundation, which explores chamber music in its historical context by presenting and producing period performances, including an annual subscription series of four Symposiums in New York featuring both performance and discussion of chamber music. She is also a Board Director of the American Hospital of Paris Foundation, which provides funding support for the operations of the American Hospital of Paris and functions as the link between the Hospital and the United States, funding many collaborative and exchange programs with New York-Presbyterian Hospital. She is also a Fellow of the Yankee Institute for Public Policy, a research and citizen education organization that focuses on free markets and limited government, as well as issues of transparency and good governance."
    positive "He was later elected chairman of the New Hampshire Republican State Committee, a position he held from 2007 to 2008. When he was elected he was 34 years old, making him the youngest state party chairman in the history of the United States at the time. His term as chairman included the 2008 New Hampshire primary, the first primary in the 2008 United States presidential election. He later served as the executive director of the Yankee Institute for Public Policy for five years, beginning in 2009. He is the author of a book about the New Hampshire primary, entitled Granite Steps, and the founder of the immigration reform advocacy group Americans By Choice."

    Briefly


    Other recent publications

    Other recent publications that could not be covered in time for this issue include the items listed below. Contributions, whether reviewing or summarizing newly published research, are always welcome.

    How English Wikipedia mediates East Asian historical disputes with Habermasian communicative rationality

    From the abstract: [3]

    "We compare the portrayals of Balhae, an ancient kingdom with contested contexts between [South Korea and China]. By comparing Chinese, Korean, and English Wikipedia entries on Balhae, we identify differences in narrative construction and framing. Employing Habermas’s typology of human action, we scrutinize related talk pages on English Wikipedia to examine the strategic actions multinational contributors employ to shape historical representation. This exploration reveals the dual role of online platforms in both amplifying and mediating historical disputes. While Wikipedia’s policies promote rational discourse, our findings indicate that contributors often vacillate between strategic and communicative actions. Nonetheless, the resulting article approximates Habermasian ideals of communicative rationality."

    From the paper:

    "The English Wikipedia presents Balhae as a multi-ethnic kingdom, refraining from emphasizing the dominance of a single tribe. In comparison to the two aforementioned excerpts [from Chinese and Korean Wikipedia], the lead section of the English Wikipedia concentrates more on factual aspects of history, thus excluding descriptions that might entail divergent interpretations. In other words, this account of Balhae has thus far proven acceptable to a majority of Wikipedians from diverse backgrounds. [...] Compared to other language versions, the English Wikipedia forthrightly acknowledges the potential disputes regarding Balhae's origin, ethnic makeup, and territorial boundaries, paving the way for an open and transparent exploration of these contested historical subjects. The separate 'Balhae controversies' entry is dedicated to unpacking the contentious issues. In essence, the English article adopts a more encyclopedic tone, aligning closely with Wikipedia's mission of providing information without imposing a certain perspective."

    (See also excerpts)

    Facebook/Meta's "No Language Left Behind" translation model used on Wikipedia

    From the abstract of this publication by a large group of researchers (most of them affiliated with Meta AI):[4]

    "Focusing on improving the translation qualities of a relatively small group of high-resource languages comes at the expense of directing research attention to low-resource languages, exacerbating digital inequities in the long run. To break this pattern, here we introduce No Language Left Behind—a single massively multilingual model that leverages transfer learning across languages. [...] Compared with the previous state-of-the-art models, our model achieves an average of 44% improvement in translation quality as measured by BLEU. By demonstrating how to scale NMT [neural machine translation] to 200 languages and making all contributions in this effort freely available for non-commercial use, our work lays important groundwork for the development of a universal translation system."

    "Four months after the launch of NLLB-200 [in 2022], Wikimedia reported that our model was the third most used machine translation engine used by Wikipedia editors (accounting for 3.8% of all published translations) (https://web.archive.org/web/20221107181300/https://nbviewer.org/github/wikimedia-research/machine-translation-service-analysis-2022/blob/main/mt_service_comparison_Sept2022_update.ipynb). Compared with other machine translation services and across all languages, articles translated with NLLB-200 has the lowest percentage of deletion (0.13%) and highest percentage of translation modification kept under 10%."

    "Which Nigerian-Pidgin does Generative AI speak?" – only the BBC's, not Wikipedia's

    From the abstract:[5]

    "Naija is the Nigerian-Pidgin spoken by approx. 120M speakers in Nigeria [...]. Although it has mainly been a spoken language until recently, there are currently two written genres (BBC and Wikipedia) in Naija. Through statistical analyses and Machine Translation experiments, we prove that these two genres do not represent each other (i.e., there are linguistic differences in word order and vocabulary) and Generative AI operates only based on Naija written in the BBC genre. In other words, Naija written in Wikipedia genre is not represented in Generative AI."

    The paper's findings are consistent with an analysis by the Wikimedia Foundation's research department that compared the number of Wikipedia articles to the number of speakers for the top 20 most-spoken languages, where Naija stood out as one of the most underrepresented.

    "[A] surprising tension between Wikipedia's principle of safeguarding against self-promotion and the scholarly norm of 'due credit'"

    From the abstract:[6]

    Although Wikipedia offers guidelines for determining when a scientist qualifies for their own article, it currently lacks guidance regarding whether a scientist should be acknowledged in articles related to the innovation processes to which they have contributed. To explore how Wikipedia addresses this issue of scientific "micro-notability", we introduce a digital method called Name Edit Analysis, enabling us to quantitatively and qualitatively trace mentions of scientists within Wikipedia's articles. We study two CRISPR-related Wikipedia articles and find dynamic negotiations of micro-notability as well as a surprising tension between Wikipedia’s principle of safeguarding against self-promotion and the scholarly norm of “due credit.” To reconcile this tension, we propose that Wikipedians and scientists collaborate to establish specific micro-notability guidelines that acknowledge scientific contributions while preventing excessive self-promotion.

    See also coverage of a different paper that likewise analyzed Wikipedia's coverage of CRISPR: "Wikipedia as a tool for contemporary history of science: A case study on CRISPR"

    "How article category in Wikipedia determines the heterogeneity of its editors"

    From the abstract:[7]

    " [...] the quality of Wikipedia articles rises with the number of editors per article as well as a greater diversity among them. Here, we address a not yet documented potential threat to those preconditions: self-selection of Wikipedia editors to articles. Specifically, we expected articles with a clear-cut link to a specific country (e.g., about its highest mountain, "national" article category) to attract a larger proportion of editors of that nationality when compared to articles without any specific link to that country (e.g., "gravity", "universal" article category), whereas articles with a link to several countries (e.g., "United Nations", "international" article category) should fall in between. Across several language versions, hundreds of different articles, and hundreds of thousands of editors, we find the expected effect [...]"

    "What do they make us see:" The "cultural bias" of GLAMs is worse on Wikidata

    From the abstract:[8]

    "Large cultural heritage datasets from museum collections tend to be biased and demonstrate omissions that result from a series of decisions at various stages of the collection construction. The purpose of this study is to apply a set of ethical criteria to compare the level of bias of six online databases produced by two major art museums, identifying the most biased and the least biased databases. [...] For most variables the online system database is more balanced and ethical than the API dataset and Wikidata item collection of the two museums."

    References

    1. ^ Rozado, David (June 2024). "Is Wikipedia Politically Biased?". Manhattan Institute. Dataset: https://doi.org/10.5281/zenodo.10775984
    2. ^ Kerkhof, Anna; Münster, Johannes (2019-10-02). "Detecting coverage bias in user-generated content". Journal of Media Economics. 32 (3–4): 99–130. doi:10.1080/08997764.2021.1903168. ISSN 0899-7764.
    3. ^ Jee, Jonghyun; Kim, Byungjun; Jun, Bong Gwan (2024). "The role of English Wikipedia in mediating East Asian historical disputes: the case of Balhae". Asian Journal of Communication: 1–20. doi:10.1080/01292986.2024.2342822. ISSN 0129-2986.   (access for Wikipedia Library users)
    4. ^ Costa-jussà, Marta R.; Cross, James; Çelebi, Onur; Elbayad, Maha; Heafield, Kenneth; Heffernan, Kevin; Kalbassi, Elahe; Lam, Janice; Licht, Daniel; Maillard, Jean; Sun, Anna; Wang, Skyler; Wenzek, Guillaume; Youngblood, Al; Akula, Bapi; Barrault, Loic; Gonzalez, Gabriel Mejia; Hansanti, Prangthip; Hoffman, John; Jarrett, Semarley; Sadagopan, Kaushik Ram; Rowe, Dirk; Spruit, Shannon; Tran, Chau; Andrews, Pierre; Ayan, Necip Fazil; Bhosale, Shruti; Edunov, Sergey; Fan, Angela; Gao, Cynthia; Goswami, Vedanuj; Guzmán, Francisco; Koehn, Philipp; Mourachko, Alexandre; Ropers, Christophe; Saleem, Safiyyah; Schwenk, Holger; Wang, Jeff; NLLB Team (June 2024). "Scaling neural machine translation to 200 languages". Nature. 630 (8018): 841–846. Bibcode:2024Natur.630..841N. doi:10.1038/s41586-024-07335-x. ISSN 1476-4687. PMC 11208141. PMID 38839963.
    5. ^ Adelani, David Ifeoluwa; Doğruöz, A. Seza; Shode, Iyanuoluwa; Aremu, Anuoluwapo (2024-04-30). "Which Nigerian-Pidgin does Generative AI speak?: Issues about Representativeness and Bias for Multilingual and Low Resource Languages". arXiv:2404.19442 [cs.CL].
    6. ^ Simons, Arno; Kircheis, Wolfgang; Schmidt, Marion; Potthast, Martin; Stein, Benno (2024-02-28). "Who are the "Heroes of CRISPR"? Public science communication on Wikipedia and the challenge of micro-notability". Public Understanding of Science. doi:10.1177/09636625241229923. ISSN 0963-6625. PMID 38419208. blog post
    7. ^ Oeberst, Aileen; Ridderbecks, Till (2024-01-07). "How article category in Wikipedia determines the heterogeneity of its editors". Scientific Reports. 14 (1): 740. Bibcode:2024NatSR..14..740O. doi:10.1038/s41598-023-50448-y. ISSN 2045-2322. PMC 10772120. PMID 38185716.
    8. ^ Zhitomirsky-Geffet, Maayan; Kizhner, Inna; Minster, Sara (2022-01-01). "What do they make us see: a comparative study of cultural bias in online databases of two large museums". Journal of Documentation. 79 (2): 320–340. doi:10.1108/JD-02-2022-0047. ISSN 0022-0418.   / freely accessible version


    ToDo List

    Miscellaneous tasks
     

    A list of articles needing cleanup associated with this project is available. See also the tool's wiki page and the index of WikiProjects.

    Categories to look through

    Conservatism articles needing attention
    Conservatism articles needing expert attention
    Wikipedia requested images of conservatism
    (See also this much larger list of relevant articles without a lead image)
    Conservatism articles needing infoboxes

    Translation ToDo

    A list of related articles particularly good and notable enough to be worthy of a solid translation effort

    Requested articles (in general)

    1. ^ Backman, J. (2022). Radical conservatism and the Heideggerian right : Heidegger, de Benoist, Dugin. Frontiers in Political Science, 4, Article 941799. https://doi.org/10.3389/fpos.2022.941799

    Merging ToDo

    A list of related articles that may have resulted from a WP:POVFORK or may, at least, look like the functional equivalents of one
    Note that the exact target of a potential merge must not be provided here and that multiple options (e.g. generous use of Template:Excerpt) might accomplish the same
    Retrieved from "https://en.wikipedia.org/w/index.php?title=Wikipedia:WikiProject_Conservatism&oldid=1263409301"