Extending Research on Deception in Sport – Combining Perception and Kinematic Approaches
Bibliographic record
Abstract
The spatio-temporal demands of many high performance sport contexts require a strategic interplay between anticipation from early kinematic cues and the appropriate movement strategy. Despite the importance of the interaction between observer and deceiver in these contexts, this dyad is usually considered separately (i.e., from perceptual-cognitive or kinematic perspectives). The present approach proposes a consolidation of perceptual-cognitive and kinematic perspectives into a dyad of deception that focuses on the interplay between opposing actors within antagonistic contexts. A framework is proposed for analyzing movement deception within this dyad. Applying a functional approach, the deceptive act is positioned as a means of optimally solving an antagonistic performance task with high spatio-temporal demands. The framework involves three elements: first, the context of the movement deception is evaluated relative to the constraints imposed by the athlete, object, and deceptive content. Together, these constraints generate a range of potential kinematic options for movement deception. Second, movement deception is determined by the spatio-temporal constraints of the original context. More simply, misleading information is only useful if it mimics elements of the genuine movement. Third, the framework emphasizes targeting the spatio-temporal interplay as well as differentiating between active and co(ntra)-active movement deception. Our goal with this framework is to supplement movement deception research by providing a conceptional context that can be applied across sports.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".