Leisure and Gender: Challenges and Opportunities for Feminist Research
Bibliographic record
Abstract
The centrality of gender as an organizing principle of leisure practice has been the focus of a considerable body or research conducted over the past couple of decades (for example, Deem, 1986; Wimbush and Talbot, 1988; Henderson et al., 1996; Wearing, 1998). Researchers revealed how gender relates not only to leisure activities and behaviours, but also to the experiences and meanings of leisure in everyday life. The gender stereotyping of activities, evident in many realms of leisure practice, was shown to be associated with gendered opportunities, constraints, and patterns of time use. Men’s time was seen as segmented with often a clear differentiation between work and non-work, and men seemed to have a greater availability of leisure activities and relaxation. The more holistic nature of women’s lives, despite dramatic increases in labour market participation of some groups of women in the later years of the twentieth century, was seen to reflect women’s caregiving roles, family responsibilities, and the lack of access to leisure that was free of socially prescribed obligations. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 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".