Report: Critiquing the critiques about media and minority research in Canada
Bibliographic record
Abstract
I want to begin by thanking the editors of this special issue for the opportunity to reflect critically upon my contributions to research in the arena of media and minority representation. I came to this field when I was a television news pro-ducer. I was struck by the lack of journalists of colour on the floor of the news-room and puzzled by the lack of critical and nuanced attention given to stories related to race. This led me to complete a review of the literature on media and minority relations when I became a postdoctoral fellow. I made recommendations for future research in this area, based on interviews I conducted with journalists, communication scholars, and leaders of not-for-profits. It has been particularly gratifying for me to see that this piece has proven valuable to other scholars who are now conducting research on this topic. I was asked to do a follow-up piece on research on immigration and media in Canada (Mahtani, 2008). More recently, my research is exploring diversity—and the complicit and complicated use of this term in newsrooms. Diversity is employed in a myriad of ways in news discourse, as diversity has been seen as not making just good moral
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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 | 0.036 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.060 | 0.033 |
| Scholarly communication | 0.032 | 0.010 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".