Mind the Gap (or Mending It): Qualitative Research and Interdisciplinarity in Kinesiology
Bibliographic record
Abstract
This article addresses the perceived gap between the humanities and social sciences, and the sciences in kinesiology faculties and departments as interdisciplinary pressures mount in an increasingly complex world. I use an historical lens to highlight past difficulties in working across the two solitudes and describe Stephen Jay Gould's efforts to mend the gap. Likening the humanities to the cunning fox and science to the persistent hedgehog, he argued that with care the two seeming opposites can be unified. I discuss how kinesiologists might follow his advice in developing more fertile collaborative interdisciplinary approaches in research, teaching, and professional training and provide some suggestions for mechanisms that might enhance the benefits of working together to bridge the divide. I conclude that we had better seek productive ways—in mutual respect and frequent conversations—to stick together in our broad and useful diversity.
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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 | 0.262 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.022 | 0.066 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".