Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".