Gendered mediation: A continued disadvantage to female politicians
Bibliographic record
Abstract
Female and male politicians tend to be treated differently by the media; these differences are both subtle and less subtle (Kahn, 1994; Sreberny-Mohammadi and Ross, 1996; Gidengil and Everitt, 1999, 2000, 2003b; Uscinski and Goren, 2011). With ‘male’ as the norm in politics, political reporting tends to be framed in a masculine narrative. As a result, the female’s behaviour is misrepresented (Sreberny-Mohammadi and Ross, 1996). A subtle way in which reporters cover male and female politicians differently constitutes what Sreberny-Mohammadi and Ross (1996) refer to as gendered mediation. Focusing on the 2012 Alberta and 2013 British Columbia election leaders' debates, this thesis assesses the extent of gendered mediation in Canadian provincial politics. It uses Gidengil and Everitt's (1999 and 2000) coding scheme in its analysis of aggressive behaviour in the provincial leaders’ debates. The results are compared with coverage to assess if female politicians' aggressive behaviour is exaggerated, and if the media continues to frame politics within a masculine narrative, emphasizing violence and conflict. This thesis uses content analysis of newspaper coverage to arrive at its conclusions about the state of gendered mediation in Canadian provincial politics. The findings suggest that that coverage of leaders' debates relies heavily on violent and conflictual imagery, and that the behaviour of female politicians is often misrepresented.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".