Building a case for infusing posthumanist thinking in the qualitative training of sport and exercise psychology researchers
Bibliographic record
Abstract
Qualitative research in sport and exercise psychology is a flourishing area of inquiry. Nonetheless, several limitations with conventional humanist qualitative research have been identified, most prominently how preformed methodologies impede a full appreciation of the complexity of existence. As a viable alternative, a posthumanist lens on inquiry has been advanced as a means of orienting thinking in a different direction. The purpose of the present article lies in building a case for infusing posthumanist thinking in the qualitative training of sport and exercise psychology researchers. Posthumanism is deployed not as a “doing away” with humanist approaches to research but as an ontological lens that instigates novel insights for how we can think qualitative training differently. The article first situates humanism and humanist education, followed by an overview of the limitations of conventional humanist qualitative research. A rationale for posthumanist thinking is offered, along with some of the fundamental tenets of posthumanism. A move toward infusing posthumanist thinking in qualitative training in sport and exercise psychology is undertaken through six suggested supervising and teaching practices: (a) encouraging graduate students to start inquiry with concepts, (b) nurturing environments where graduate students can readthinkwrite, (c) training graduate students to reposition voice, (d) exposing graduate students to the importance of thinking beyond the human, (e) reimagining the role of the supervisor/teacher, and (f) inspiring graduate students to compost and make kin. In the concluding thoughts, two interrogations relating to the neoliberal university and the Anthropocene are raised that posthumanist thinking can help situate differently.
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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 | 0.580 | 0.412 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.029 | 0.145 |
| Scholarly communication | 0.025 | 0.038 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.016 | 0.026 |
| 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".