Advancing narrative research in sport psychology: Reflections on different kinds of stories
Bibliographic record
Abstract
Although narrative inquiry research was less common in the sport sciences in the early 2000s, the study of stories contextualised in narratives is now embraced in sport psychology research. In this commentary, we build on recent discussions and review papers by reflecting on the current position of narrative inquiry research in the sport sciences and sport psychology. Our precise aim was to explore a growing body of narrative research in sport psychology focusing on public stories (i.e. autobiographies, digital media) to further advocate for their value to study identities and sporting lives. To contextualise our commentary, we first outline several narrative inquiry assumptions (e.g. distinctions between a story and narrative, onto-epistemology, story analyst vs. storyteller, big and small story approaches). Next, we provide examples of research on ‘different kinds of stories’ circulated in public spaces for witness and wider audience consumption to explore their value in narrative inquiry research in sport psychology. We then reflect on future research directions and considerations to advance critically informed and principled research on autobiography and digital media as storied resources in narrative inquiry.
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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 | 0.050 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".