Leisure Matters: Cross Continent Conversations in a Time of Crisis
Bibliographic record
Abstract
Months after COVID-19 emerged as a newsmaker in Asia, a new strain of March Madness emerged in North America. Incredulity followed as leisure activities, hallowed as venues and expressions of individual and collective identity were closed. Freedoms, real and perceived, were curtailed. Like others, we sought to maintain social connections. For the first time in decades, our weekly on-line conversations became normative. Two authors remain working to sustain the academy’s work during this crisis and the other is retired. Spatially we reside in a major metropolitan area of 6 million, a small west coast college town, and a Great Lakes region vacation community. Our discussion connects leisure research and the context of basic rights that North Americans have long taken for granted. This commentary emerged from integrated discussion regarding how the crisis affects and may change leisure behavior from multiple perspectives.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.016 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".