Bibliographic record
Abstract
The article is concerned with the difficulty of providing leisure today with a positive definition that goes beyond merely being a negation of work. I argue that the vague boundaries between work and leisure play into the hands of work – a highly praised activity that is dominant in today’s society. I argue that in such a situation, education as leisure and as good in itself is hard to conceive and sustain. First, I present the concept of leisure in ancient Greece (scholé) as time dedicated to autotelic activities – activities taken for their own good – a definition that remains paradigmatic despite its later impossibility. I then show that once work has transformed from hated to bearable to eventually understood as good from a moral perspective, the concept of leisure has also changed – so much so that its positive definition is no longer available to us. After showing how education is affected by the diffusion of the boundaries between work and leisure, I suggest three possible ways to counter this process: (1) focusing on leisure as resistance to the dominance of work, (2) appealing to the deep connection between leisure and religious worship, and (3) a radical rejection of the concept of “leisurely work” or any other kind of work that is presumed good in itself.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".