Kinanthropometry in Brazilian Physical Education (1970s): a new knowledge perspective for this field
Bibliographic record
Abstract
Abstract The aim of the present study is to understand the constitution of kinanthropometry as scientific disciplinary field in the 1970s in Brazil. Therefore, a bibliographic review was carried out in nine databases and in a specific journal focused on publications from the Kinanthopometry perspective, since this is an element to legitimize a scientific discipline. Only two studies dealing with this topic were selected. Given such a gap in the literature, three interviews with professors who organized the Physical Education (PE) course laboratories, as well as a newspaper report from the period, were used in the study. Different names have been associated with the scientific field of human composition assessment throughout history, as well as formulating different body perceptions, such as Biometrics, Anthropometry and kinanthropometry. Each of these factors determine relationships with the involved socio-cultural context. Such a complexity to understand a conjecture within a historical time expands the space available for analyses. In the 1960s, the term kinanthropometry emerged in foreign countries as a new way of interpreting human body composition assessments linked to knowledge in the PE field based on movement and anatomy. This term was imported by Brazilian researchers after their contact with scientists in USA and Canada, since it offered the possibility of acquiring new representations for research in the PE field back in the 1970s.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".