Are we afraid of our selves? Self-narrative research in leisure studies
Bibliographic record
Abstract
During the past decade, leisure scholars have started becoming more comfortable with applying to their work newer approaches to knowledge and research methodologies, many of which are being utilized in other social sciences. Self-narrative research, or autoethnography, is a form of inquiry that raises questions regarding separations and interactions of personal and professional identities. While personal narrative research has been in use since the 1980s, we argue that it remains unfamiliar and/or personally and professionally threatening to many leisure scholars. This article builds from leisure scholars' recent discussions on the influence of poststructuralism, narrative inquiry, and the need for reflexive methodologies within leisure research. We extend this discussion one step further, highlighting the need for self-narrative research in leisure. We then outline the ensuing benefits and ethical ramifications of such research, which we believe is capable of making unique contributions to leisure studies.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".