Bibliographic record
Abstract
he initial discussion leading to the development of this study originated some thirteen years ago when Mark and Diane were faculty members in the Department of Leisure Studies and Services at the University of Oregon.Ironically, shortly thereafter we were, along with approximately 100 other untenured colleagues, both pink slipped as part of the University's reaction to a state-imposed twelve percent budget cut.We were fortunate to both secure positions at other universities before our Oregon positions were eliminated.Nevertheless, the very real feeling of being devalued professionally and the sense of loss with respect to our colleagues and academic community were sobering.The downsizing experience gave us renewed resolve and added empathy with respect to studying unemployment and leisure.We are grateful for the financial support of the Ontario Ministry of Tourism and Recreation, which made this project possible.Also, the gracious support of the staff, especially Barry Daniels, at the Canada Employment Centre was important in the early stages of recruitment and data collection.We would like to acknowledge a number of individuals who have contributed to this research project over the past few years.The principal research assistant for the data collection and data organization phases of the study was Laurie B. Whyte, then a graduate student in the Department of Recreation and Leisure Studies, University of Waterloo.Data collection, entry, organization, and analysis assistance was provided by numerous students and staff, unless otherwise noted, who were at or are from the University of Waterloo's Department of Recreation and Leisure Studies.This assistance was invaluable in informing the authors as we developed the final product.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.373 | 0.237 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".