Bibliographic record
Abstract
Contributors to the literature on gamesmanship typically assume that gamesmanship can be clearly distinguished from other legal strategies used in sports. In this article, we argue that this is a mistake. Instead, we propose that gamesmanship is a form of strategic excellence and a proper part of competitive sport. Using Howe’s influential work on gamesmanship as a representation of the received view, we show how the current debate rests on a presupposition that fails to withstand critical scrutiny (Section 2). Further, we argue that once this distinction is shown to be untenable, Howe’s evaluative account of gamesmanship fails (Section 3). By contrast, our alternative analysis leads us to a more positive evaluation of gamesmanship. In particular, we contend that effective uses of gamesmanship are simply examples of strategic excellence that – by definition – fall within the boundaries of what is permissible in competitive sport. We conclude by considering the relationship between gamesmanship and the spirit of the sport (Section 4) and by addressing a potential criticism that draws an analogy between sport and professional practices that clearly do not permit strategies akin to gamesmanship (Section 5).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.053 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".