“Old WASPs” and “middle-class white ladies”: what columnists’ self-identification says about diversity in Canadian newsrooms
Bibliographic record
Abstract
In the last two decades, as Canada’s demographics have shifted, Canadian news publications have struggled to reflect increasing diversity in both their newsroom makeup and their content. In Canada, news organizations have historically resisted instituting a process to examine their staff composition. In the absence of consistent self-reporting data, this study aims to fill in important information about Canadian newspaper newsroom composition by focusing on data that is actually self-reported by opinion journalists from within their publications. Using phrases from columnists’ published work, this study examines how far the demographic makeup of columnists at Canada’s three largest national newspapers by circulation reflects the diversity of the Canadian population at large. Our findings indicate that while representation of women columnists at the Toronto Star, The Globe and Mail and the National Post improved over the 21-year period of our study, white columnists – regardless of gender – became significantly more overrepresented, in sharp contrast to census data over the same period. The absence of Indigenous columnists and Black women columnists is especially significant at a time when the discourse around Indigenous issues and anti-Blackness has increased. This paper concludes that while Canada’s population grew increasingly diverse over this period, the gulf between the country’s demographics and those who write on key Canadian issues widened. As a result, a large part of the Canadian public remains underrepresented by major news publications. This paper draws connections between the lack of self-reporting and its potential to impact voice and agency in the newsroom.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".