A Comparative Benchmark Analysis of Recreation Programs in Türkiye, the United States, and Canada Using a ChatGPT-Based Interview Approach
Bibliographic record
Abstract
ChatGPT has established a significant position among large language models (LLMs) and has become widely utilized by both users and researchers. While the number of studies on ChatGPT's applications, particularly in fields such as education and tourism, is steadily increasing in the literature, research on its use in recreation remains notably limited. Moreover, no studies have benchmarked recreation programs against those in developed countries. Accordingly, this study aims to benchmark recreation programs in Türkiye, the United States, and Canada by comparing admission requirements, program curricula, graduate competencies, and employment conditions. Furthermore, it seeks to provide a future-oriented projection and propose a certification program tailored for Türkiye. Employing an interview technique facilitated through ChatGPT, the study’s findings highlight differences in student admissions, curricula, graduate competencies, and employment prospects across recreation programs in Türkiye, the United States, and Canada. Based on these differences, the study presents recommendations for the field of recreation in Türkiye, a ten-year projection, and a proposed certification program. The research offers both theoretical insights and practical implications.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".