Bibliographic record
Abstract
ChatGPT has injected artificial intelligence (AI) into text-creating academic fields. In this article we explore the issues around ChatGPT in the history of sport. We begin by asking ChatGPT to analyze and evaluate 271 book reviews in Sporting Traditions, the official journal of the Australian Society for Sport History. In 2022, we published an analysis of the same reviews conducted by human reviewers to assess their scholarly and political contributions. We used a precise formula and in this research we instructed ChatGPT to use the identical formula. We then compared the two sets of results in what amounts to a methodological and political assessment of ChatGPT. Our comparison supports the broader scholarly consensus that ChatGPT is useful for creating summaries. Regarding the political function of book reviews and the challenge that reviewers face in navigating between critique and collegiality, ChatGPT deemed that reviewers are typically balanced. Nonetheless, we argue that ChatGPT has limitations in accurately categorizing core components of academic book reviews and is unable to replicate the critical reflection shown by skilled, knowledgeable and experienced human reviewers. We also recommend that journal editors require book reviewers to declare upon submission that they did not use AI in their review.
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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 | 0.054 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.026 | 0.029 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".