Bébé-boumeurs bénévoles ? Les freins et les leviers de leur engagement
Bibliographic record
Abstract
Once the preserve of the young and fit, fitness centres now offer a diverse range of activities to cater to all age groups. This reflects a change in the clientele, which now includes « older » women. Healthy life expectancy has risen markedly over the past 20 years (INSEE, 2010). A preoccupation with ageing (Feillet, 2012), a desire to maintain an image (and body image) for themselves and in the eyes of others (Corbin, Courtine, Vigarello, 2011), a need to « stay looking young » (Amadieu, 2002) and the emphasis on youth in the media (Kaufmann, 2001) are just a few of the factors behind the broadening of the ages at which women participate in fitness activities out of a concern for their bodies and self-presentation. In the space of a few years, fitness has become a « tool » for preventing or mitigating ageing for women who wish to conform to prescribed social norms.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".