Re-Immersing Into Elite Swimming Culture: A Meta-Autoethnography by a Former Elite Swimmer
Bibliographic record
Abstract
This paper presents two meta-autoethnographies written by a former elite swimmer. In the first metaautoethnography, the swimmer revealed doubts in relation to details, emotions and inner-thoughts that she had included in her historical autoethnographic work. As a means of sorting and pondering these tensions and uncertainties, the swimmer explored cultural re-immersion as a possible additional element in the metaautoethnographic process. The second meta-autoethnography centers on the swimmer’s re-immersion into elite swimming culture. It was revealed how cultural re-immersion enabled the swimmer to better reflect on her historical autoethnographic work by providing a more conscientized, rational and reflexive voice. This research highlights how cultural re-immersion should be considered as an additional element in the metaautoethnographic process as it benefits both the author and also audience.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".