Número 410 suplemento: resúmenes del Congreso Internacional AIESEP 2015. Año LXVII 3er trimestre, 2015, nº 8 supl., VI época
Bibliographic record
Abstract
In recent decades, the fitness, sport and leisure industries have boomed, whilst levels of inactivity and obesity have continued to rise (Smith Maguire, 2008).In 2013, the global health club industry generated annual revenues of $77.5 billion and served 140 million users (International Health, Racquet & Sportsclub Association [IHRSA], 2014).There has been very little pedagogical research on the practice and professional development of the 'fitness professionals' working at the heart of this industry, yet this group is an important part of the lifelong physical activity education landscape. METHODThe purpose of this paper is to critically explore the role of fitness professionals as public health assets, in theory and in practice.In section one, we report the findings of a comprehensive review of literature on the ways in which fitness professionals have become implicated in public health agendas and the training/development that is available to them
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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; both teacher heads 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".