Journeying into postqualitative inquiry: an exploration of the opportunities and tensions for graduate students in sport and exercise psychology
Bibliographic record
Abstract
Postqualitative inquiry (PQI) has emerged in recent years as a potential-filled approach to inquiry for researchers seeking to move beyond humanist paradigms such as postpositivism and interpretivism. PQI offers researchers exciting opportunities to shift from method-focused scholarship to theory-focused scholarship, amongst other moves. As a graduate student conducting research in sport and exercise psychology (SEP), PQI caught my (i.e., first author) attention and drew me into the dynamic whirlwind of immanence, materiality, and relationality. In the present commentary paper, the purpose is to explore the opportunities and tensions of PQI for SEP graduate students. Specifically, I do so by sharing my experiences engaging with PQI in attempts to help other SEP graduate students navigate their PQI journeys. The paper first situates what PQI is/is not and what it does, followed by some of the opportunities related to engaging in PQI, which include stimulating intellectual openness, working towards social justice, and addressing methodological critiques. Despite these opportunities, some tensions do exist, such as the labours of reading hard, onto-epistemological juggling, and the rush to application. In grappling with these opportunities and tensions, recommendations are offered to SEP graduate students who do decide to journey into PQI. These recommendations consist of reframing the return on investment as rhizomatic, remaining aware of the exclusionary effects of labelling PQI, and understanding the ramifications of a decentred human in SEP research. Concluding thoughts are offered in the form of reflections from both the first and second authors.
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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 | 0.041 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.047 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.006 | 0.021 |
| 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".