Representations of Political Leadership Qualities in News Coverage of Australian and Canadian Government Leaders
Bibliographic record
Abstract
ABSTRACT How do the media depict the leadership abilities of government leaders, and in what ways are these depictions gendered? Does the focus of leadership evaluations change over time, reflecting the increased presence of women in top leadership roles? To answer these questions, we examined news coverage of 22 subnational government leaders in Australia and Canada, countries in which a significant number of women have achieved the premiership at the state or provincial level since 2007. Analysis demonstrates that newly elected women and men leaders receive approximately the same number of leadership evaluations. Women are assessed based on the same criteria as men. All subnational political leaders are expected to be competent, intelligent, and levelheaded. That journalists prioritize experience and strength while downplaying honesty and compassion indicates a continued emphasis on “masculine” leadership norms in politics. Yet evaluations of new premiers have emphasized the traditionally “feminine” trait of collaboration as key to effective leadership and, over time, have given more attention to likability and emotions when covering male premiers. As our analysis reveals, media conceptualizations of political leadership competencies are slowly expanding in ways that make it easier for women to be seen as effective political leaders.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".