‘From the garden’ banquet address given on 25 May 2023, at CCLR17, University of Ottawa
Bibliographic record
Abstract
I was invited to speak at CCLR17 in 2023 and reflect on leisure research from the late 1970s through my career, drawing on my participation in CALS and many of the past CCLRs. I view CCLRs as the public manifestation of the state of leisure research/scholarship. In 2000 after CCLR9, which I chaired, I wrote a commentary piece for Leisure/Loisir ’Reflections from the Garden’ reflecting on a 1981 piece by Steve Smith ‘Worlds Apart: Thoughts on a Canadian Association of Leisure Studies‘ wherein he posited that the muse of leisure studies could create several possible environments for leisure researchers: ‘a desert, a jungle or a garden’. At that point I chose to consider leisure studies as a garden in which in Smith’s view would produce ‘bountiful crops year after year‘ and be ‘open to new way, new ideas, new individuals’. In this 2023 speech I again addressed the question ‘have we become a desert or a jungle or a garden’? I focused on the range of disciplines, the variety of backgrounds of the scholars, the demographics of both the researchers and their chosen subjects, and the role of CCLRs in developing fellowship and cooperation in leisure research.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.598 | 0.196 |
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".