History on the Web / L’Histoire sur la toile
Bibliographic record
Abstract
Sports headlines over the past few years remind us of the ways\nthat race and sports intersect in both the past and the present.\nWillie O’Ree’s induction into the Hockey Hall of Fame, NFL\nplayer protests, and ethnic slurs hurled at First Nations hockey\nplayers, among others, all suggest that sports can offer insights\ninto racial injustice in society, as well as the fight against it. Over\nthe past three years, I have been part of a university-community\ngroup that has developed a website and public history project to\nhelp students explore these issues. In June, 2017, we launched\n“Breaking the Colour Barrier: Wilfred “Boomer” Harding and\nthe Chatham Coloured All-Stars” (http://cdigs.uwindsor.ca/\nBreakingColourBarrier/). In 1934, the All-Stars were the first\namateur Black baseball team to win a provincial championship\nin the predominantly white Ontario Baseball Association. Like\nmany athletes from historically-marginalized communities, the\nplayers regularly faced racial and economic barriers playing\nball in 1930s southern Ontario. While their descendants and\ncommunity members have remembered and commemorated\nthe team’s achievements and hardships, the All-Stars’ story has\nreceived little attention outside Chatham.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.240 | 0.092 |
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".