Bibliographic record
Abstract
AbstractThe purpose of this paper is to provide a conceptual argument for why leisure researchers, and feminist leisure scholars in particular, should examine how a technology enhanced form of leisure, namely reading sexually explicit material, can liberate or constrain women's sexuality (Sonnet, 1999). To achieve this goal, we examined the popular Fifty Shades of Grey series, which is largely consumed by women, utilizing various technologies. Situating our analysis within the broader literature on leisure and technology and feminism and sexuality, we argue that understanding women's consumption of erotic and pornographic materials during their leisure has complex and important implications for women's sexuality and subsequent well-being. In so doing, we point to a number of areas for future research that will help complicate this understudied area of leisure research (Freysinger et al., 2013).Keywords: sexually explicit materialmediated leisurefeminismparticipatory culturesserious leisure
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".