InTrack project ˗ Theoretical framework, design, and methods: A study protocol
Bibliographic record
Abstract
Sports performance is the result of a complex interaction between individual and environmental factors. The purpose of this paper is to explain the methods used in the InTrack Project, a cross-sectional and cross-cultural project developed to investigate the variance in the performance of runners from different countries and to understand whether the differences in the performance can be explained by micro-level (athletes characteristics and proximal environment), meso-level (the distal environment that plays a relevant role on the relationships established at micro-level), and the macro-level (environmental features that shape countries characteristics). The sample will be comprised of runners, of both sexes, from four countries. Data collection will be performed in two steps: i) Individual information and ii) Country-level information. At the individual level, data will be obtained from an online survey. At the country level, characteristics data will be obtained from the secondary data available (demographic, social, and economic variables). Statistical procedures expected to be used include multilevel analysis, latent class analysis, addictive and multiplicative interaction in regression models. This wealth of information is of relevance to fill gaps regarding the existence of variables to connect different levels of information, and to provide scientific support about environmental characteristics important to predict runners' performance within and between countries.
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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 | 0.078 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.017 |
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".