What the dinosaurs forgot to tell us about sport and exercise psychology
Bibliographic record
Abstract
In the early 1960s, Physicist Richard Feynman gave a famous series of lecture on physics. In one of his lectures he posed the following question, "If, in some cataclysm, all of scientific knowledge were to be destroyed, and only one sentence passed on to the next generations of creatures, what statement would contain the most information in the fewest words?" In this symposium the above question is posed to a number of faculty in the field of sport and exercise psychology who are currently researching and teaching at Canadian universities. The symposium will start with a 10 minute overview of symposium, including an introduction to the question, in a presentation titled "All Things are Made of Atoms", by Kent Kowalski (University of Saskatchewan). Twelve faculty will then each present their answers to the proposed question across a series of 5 minute presentations. Participating faculty include: Wendy Rodgers (University of Alberta), Catherine Sabiston (University of Toronto), Craig Hall (University of Western Ontario), Jean Cote (Queen's University), Tara-Leigh McHugh (University of Alberta), Kim Dorsch (University of Regina), Nick Holt (University of Alberta), Diane Mack (Brock University), John Spence (University of Alberta), Katherine Tamminen (University of Toronto), Phil Wilson (Brock University), and Kent Kowalski (University of Saskatchewan). Leah Ferguson (University of Saskatchewan) will then provide a 10-minute response as a discussant in which she reflects on the value of the symposium and the answers provided by the participating faculty members. The symposium will conclude with a 10-minute question and answer period with the audience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".