Bibliographic record
Abstract
There is a growing body of work examining gender stereotypes in media representations of female candidates, but much of this literature is based on analysis of media sources in developed countries, including the United States (Braden 1996; Jalalzai 2006; Kahn 1994, 1996; Smith 1997), Australia (Kittilson and Fridkin 2008), Canada (Kittilson and Fridkin 2008), France (Murray 2010b), and Germany (Wiliarty 2010). The increase in female presidential candidates and presidents in Latin America has encouraged research on media portrayals of women in Argentina, Chile, and Venezuela (Franceschet and Thomas 2010; Hinojosa 2010; Piscopo 2010; Thomas and Adams 2010). To date, however, there has been little research exploring media representations of female politicians in Africa. (Exceptions include Adams 2010; Anderson, Diabah, and hMensah 2011). A question that emerges is whether the gender stereotypes common in coverage in the United States, Europe, and Latin America are also prevalent in Africa.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".