A framework of cognitive biases that might influence talent identification in sport
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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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".