Gendered and Racialized Portrayals of the Governor General: Newspaper Coverage of Canada’s Head of State
Bibliographic record
Abstract
News coverage helps to define public expectations of a newly appointed Governor General and the roles that they are expected to assume in Canadian society. To date however, there are no studies examining the media’s portrayals of the Governor General despite the fact that as Canada’s vice-regents, they are increasingly appointed to represent a more diverse Canadian population. To assess this role, this paper conducts a content and discourse analysis, of the Globe and Mail’s coverage of the past five Governors General, Jeanne Sauvé, Ramon Hnatyshyn, Romeo LeBlanc, Adrienne Clarkson and Michaëlle Jean. It is argued that the media frames Governor General’s as novelties by highlighting their “first ” qualities. For example, since 1984, three of the five Governors General have been women and this plays an important role on how each Governor General is presented by the media; thus, viewed by Canadian society. The coverage of race and ethnicity is also an important component in the coverage of the Governors General since four of the Governors General have either an ethnic or racial background. Finally, it suggests that the combination of gender and race increases the media’s framing of Governors General as novelties and this can have negative implications for the manner that Adrienne Clarkson and Michaëlle Jean are reported in.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".