Are we afraid of our selves? Self-narrative research in leisure studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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.
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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it