Reflections on Critical Sport History in the Museum: Opportunities and Challenges from a Local/Regional Project
Bibliographic record
Abstract
Abstract As sport historians we choose to engage in public history projects and thus are faced with both opportunities and challenges. Our involvement in the Alberta Sports History Project provides an opportunity to engage in the ongoing discussion about how members of the academy can effectively move into the realm of public sport history. By involving oneself with a museum or sports hall of fame a historian must recognize that her/his own expectations for the work they produce and partnering groups will not always be congruent. In this research note, we draw on specific examples from the experiences arising from our role in envisioning, researching, and working to produce a history of Lethbridge, Alberta’s sporting past. We suggest that the importance of recognizing and embracing the inherently imperfect nature of public sport history provides historians of sport an opportunity to assist with the important work of preserving, documenting, and exhibiting our sporting past.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.053 | 0.050 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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".