Bibliographic record
Abstract
Some work, called here “devotee work,” is so attractive that it is essentially leisure for those engaging in it. For them the only important difference between their work and what their counterparts in serious leisure do is that devotee workers get paid for their efforts. Occupational devotees turn up chiefly, though not exclusively, in four areas of the economy, providing work there is, at most, only lightly bureaucratized: certain small businesses, the skilled trades, the consulting and counselling occupations, and the public- and client-centered professions. In short, occupational devotees and serious leisure enthusiasts intensely love the same activity, finding there a powerfully attractive work or leisure career. Thus work and leisure are, contrary to conventional wisdom, neither wholly separate nor mutually antagonistic spheres of modern life. The close relationship, examined here between serious leisure and occupational devotion demonstrates that there can be joy in work just as in leisure and that this joy is, at bottom, qualitatively the same in both worlds. In other words such joy is basically a shared sentiment, in that the core activities in work and leisure which are so powerfully attractive – and which foster the joy – are highly similar, and in some instances, literally identical.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".