Costing Participation in Sport: The Best Option Dilemma of a Student-Athlete
Bibliographic record
Abstract
Rebecca Griffin, a student-athlete, is coming off the best off-season training program of her 8 years as a rower. She is highly motivated knowing that a strong summer season could propel her to be both a major contributor to her university team in the fall and a contender to make it to the National Under 23 team. However, before she can pursue her athletic dreams, she needs to decide where she is going to row this summer and figure out if she can afford to pay for it. She needs to assess and consider the benefits and drawbacks of her summer rowing club options. Her considerations include club registration fees, travel, equipment, coaching, and competition entry costs for each, and how they will contribute to her career goals in rowing. Therefore, while working toward her goals, Rebecca must consider the affordability of her options, and make the best decision she can.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".