Infusing ‘post’ thinking in qualitative training using material-performative pedagogies
Bibliographic record
Abstract
The paper infuses post thinking in qualitative training by providing a concrete example of how students can learn to inquire through the posts in a graduate-level methods course. The paper begins by storying ‘the qualitative course’, outlining its structure and rationale. The qualitative course’s main post assignment (i.e. psychogeographic walk and performance) is then detailed and positioned as a material-performative pedagogy offering encounters that can transform students if they open themselves to the vulnerabilities and opportunities of inquiring on-the-move. Key next steps for the qualitative course are presented in terms of changing the name of the assignment, adding an object assignment, exposing students to the virtues of errant reading, inquiring as an act of creation and inquiring as epistemic witnessing. Implications for qualitative inquiry in sport, exercise, and health are offered that include attending to the relational ongoingness of existence, creating alternate understandings of understanding, and attuning to presences/absences. The paper concludes with thoughts on our collective response-ability to promote ontological pluralism. How we train the next generation of qualitative inquirers to play the ontological politics game has immense implications for presencing the manifold thoughts, concepts, and worlds that remain endarkened.
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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 | 0.142 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".