Internal social processes of discipline formation: The case of kinanthropometry
Bibliographic record
Abstract
In 1972, the term 'kinanthropometry', derived from the Greek words 'kinein' (to move), 'anthropos' (human) and 'metrein' (to measure), was launched in the international, Francophone journal Kinanthropologie by the Canadian William Ross and the Belgians, Marcel Hebbelinck, Bart Van Gheluwe and Marie-Louise Lemmens. The authors defined this neologism as 'the scientific discipline for the study of the size, shape, proportion, scope and composition of the human being and its gross motor functions'. Presenting a theoretical framework for the analysis of the internal social processes of discipline formation - derived from the social history-of-science tradition - this article critically examines whether kinanthropometry was indeed promoted and developed by its community members as a scientific discipline. Therefore, the focus will be on its conceptualisation and positioning within the field of kinanthropology/kinesiology and on its development by a scholarly association, i.e. the International Working Group on Kinanthropometry (IWGK). The strong emphasis of the kinanthropometry community on the standardisation of measurement techniques and its practical and professional application hampered its disciplinary development. Findings of this study could serve as a basis for future 'fundamental' investigations addressing questions of disciplinary development within the field(s) of physical education, kinesiology and sport science(s).
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.054 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".