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Record W4285350465 · doi:10.51952/9781529204957.ch005

The Leaders: ‘Iron Ladies’ and ‘Dangerous’ Women

2021· book-chapter· en· W4285350465 on OpenAlexaboutno aff
Emily Harmer

Bibliographic record

VenueBristol University Press eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

It took until 1979 for a woman to lead a major political party into a British general election. Since then, an uptick in the number of female leaders offers a chance to assess the way women party leaders are represented in newspaper coverage. Since Margaret Thatcher’s first campaign as Conservative Party leader in 1979, there have been five campaigns in which women leaders have been the subject of press attention. This chapter will therefore focus on these five elections. For the first three, 1979, 1983, and 1987, Margaret Thatcher was the only female leader. In the 2015 and 2017 campaigns, multiple women leaders were visible in the news, including the Conservative’s Theresa May (2017), the Green Party’s Natalie Bennett (2015) and Caroline Lucas (2017), Plaid Cymru’s Leanne Wood (2015 and 2017), and the Scottish National Party’s (and first minister of Scotland) Nicola Sturgeon (2015 and 2017). Of these elections, three were contested by female prime ministers (1983, 1987, and 2017). While the 2010 election was contested by a female party leader (Caroline Lucas, Green Party), this campaign could not be included because she did not appear in the sampled newspaper coverage. Given that party leaders have a much higher public profile than their female colleagues, it would be significant if this effects how they are reported on in gendered terms. There is some evidence suggesting that as women in Australia and Canada become increasingly prominent, news coverage about them is less likely to centre their gender identity (Trimble et al, 2019).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.929
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.235
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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