Bibliographic record
Abstract
Ross PerigoeThe issue of racism has touched me personally.As a child in the mid-1950s, I accompanied my family on our annual drive from Canada down the eastern seaboard of the United States to Florida for our Christmas vacation.I was introduced to the visible and visceral sides of racism in the American South: "whites only" drinking fountains, segregated schools for black children who were my age, and segregated bathrooms in bus stations and hotels.My parents were unflinching in their insistence that I witness the injustice to which our fellow human beings were subjected by people whose skin colour was the same as mine.The civil rights movement in the United States became a regular topic at our dinner table in the late 1950s and 1960s even though we lived in an almost exclusively white neighbourhood in Toronto.Having explored the issues of racism and representation in the early 1990s (Perigoe and Lazar 1992), I found myself returning to them after a series of books and articles by Canadian researchers rekindled my concern.The first was John Miller's (1998) Yesterday's News: Why Canada's Newspapers Are Failing Us.Miller, the former chair of journalism at Ryerson University in Toronto, fearlessly examined the state of journalism in Canada, and I found myself asking whether I too had become too comfortable within the establishment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.486 | 0.266 |
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".