Is There a Global Narrative for Kinesiology?
Bibliographic record
Abstract
At the National Academy of Kinesiology’s annual meeting in 2023, four International Fellows shared their insights into whether there is a global narrative for kinesiology. Panelists comprising Fasting (Norwegian, sport sociology), Jacobs (Canadian, exercise physiologist), Macdonald (Australian, pedagogy), and Tsung-Min Hung (Taiwanese, sport and exercise psychology) spanned both subdisciplines and continents. This paper represents a synthesis of their thinking, complemented with more incidental views from a range of scholars who accepted an invitation from Macdonald to contribute brief perspectives. Framing the paper are the concepts of globalization and its tethered process of neoliberalization, the latter argued to be a dominant ideology in many Western democracies that shapes the priorities of educational institutions. We conclude that the term “kinesiology” is not universally deployed to reference the discipline, although global narratives related to program priorities, knowledge status, metrics, and professionalization in the four continents represented exist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| 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".