A framework of cognitive biases that might influence talent identification in sport
Why this work is in the frame
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Bibliographic record
Abstract
Cognitive biases impair effective talent identification in sport and thereby can impact the long-term success of sport organisations. However, a framework is lacking to identify and overcome those biases. The aim of this paper was to develop a framework of cognitive biases that could influence talent identification in sport. We reviewed the scientific and popular literature and identified 38 biases that we rated likely to impact decisions when making judgements of talent. We used cluster analysis to classify the biases into a taxonomy of five clusters: (1) sequential effects that might influence decisions based on the order in which information occurs (e.g. the anchoring bias); (2) presentation effects that could influence decisions according to how information is presented or gathered (e.g. the framing effect); (3) cognitive models that may influence decisions according to the observer's mental understanding of the world (e.g. confirmation bias); (4) association effects that could influence decisions according to (often false) relationships identified by the observer (e.g. correlation bias); and (5) egocentric effects that might influence decisions according to the observer's view of themselves and their position in society (e.g. bandwagon effect). The results provide a framework for uncovering biases that might influence talent identification in sports.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it