Bibliographic record
Abstract
Leisure researchers work within a contested landscape for evaluating the quality and significance of their work, thereby necessitating the need to do both rigorous and socially relevant research. The 2014 Butler Lecture tackled the issue of research relevance by advancing three connected pathways to social impact: knowledge mobilization, encouraging critical reflection, and advancing social innovation. In so doing, it positioned leisure research—both its process and benefits— as a vehicle for meaningful engagement between researchers and users; it advocated for research as the conceptual, intellectual, and evidential bases for introspection, which enables researchers to insert themselves and their values into pluralistic (or complex, multilateral) dialogues; and promoted research as a crucial contributor to the change process. Accordingly, recommendations were made to encourage greater social impact, including continuing to reimagine leisure studies and its role in helping society understand, confront, and address complex social challenges.
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 | 0.046 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".