Exploring the Relative Importance of Factors That Influence Student-Athletes’ School-Choice Decisions: A Case Study of One Canadian University
Bibliographic record
Abstract
Understanding salient factors influencing student-athletes’ decisions to attend particular university institutions is of crucial importance to scholars and athletic administrators. Consequently, our research was concerned with two separate but interrelated substantive and methodological objectives: i) to gain insights into the relative importance of 12 school choice decision-making factors influencing Canadian student-athletes; and ii) to explore the efficacy of a multicriteria decision-making (MCDM) method for analyzing data in the context of the current investigation. Specifically, we employed the Analytic Hierarchy Process (AHP) to better understand the relative importance of school choice decision factors. The results of the AHP analysis on Canadian student-athletes’ school choice decisionmaking showed that having the desired academic program was the most important influence. This item was almost twice as important as the reputation of the school, and over twice as important as scholarship value, athletic facilities, chance to win, and reputation of the head coach. Of the 12 factors considered, these six had the greatest influence on student-athletes’ decision-making. Implications of our findings for research and recruitment efforts are discussed.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".