Speaking of the self and understanding physical activity participation: what discursive psychology can tell us about an old problem
Bibliographic record
Abstract
Since McGannon and Mauws' article on discursive psychology and adherence to physical activity, papers have extended the dialogue towards developing associated qualitative research methods to understand sport and exercise. The present article furthers this dialogue in the context of understanding the self and women's physical activity participation using discursive psychology and discourse analysis. An example of discursive psychology ‘in use’ was employed to theorise women's physical self (i.e. who they are) and physical activity behaviour as a collection of conversations within broader discourse(s). The power relations perpetuated by a micro‐talk within discourses also contributed towards theorising a discursive psychological view of the self and physical activity participation. The implications of a discursive psychological view of the self combined with discourse analysis for understanding women's physical participation are discussed within the context of this example.
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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 | 0.056 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.016 | 0.165 |
| Scholarly communication | 0.023 | 0.053 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".