Remembering “The Forgotten Games”: A Reinterpretation of the 1954 British Empire and Commonwealth Games
Bibliographic record
Abstract
Certainly for me (Jackie MacDonald), it was far and away the biggest event of any sort that I had been involved in until then. The trip from Toronto was the farthest I had ever travelled: Vancouver was beautiful with the mountains in the background; the local population was bursting with pride and enthusiasm for the Games; I was awed by the sight of so many famous athletes and excited by the opportunity to meet participants from all over the world. There were highs and lows of course: on the final day the “Miracle Mile” lived up to all the tremendous hype, but the horrifying spectacle of marathoner Jim Peters staggering, collapsing, then crawling on the track, and unable to finish was a tragic sight. For me personally, winning the silver medal in the women’s shot put with a personal best was the high point, while being scratched from the discus competition was the low point. I was reminded of how thrilling it was for me to be on the Canadian team in 1954 when my husband, our two sons and I went to Victoria for the 1994 Commonwealth Games. Watching the track and field events I was very touched when my older son said: “Looking at these athletes, I can picture you down there competing forty years ago.”
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".