Bibliographic record
Abstract
Elite cricket batters are often faced with the intimidating task of hitting balls arriving at speeds that can reach up to 140 km per hour. When the Sri Lankan cricketer Kumar Sangakarra was asked how he manages such a feat, he explained that, ‘Basically in batting, you have to be mindless. You’ve done all the practice, you have the muscle memory, and your reflexes are more than quick to deal with any kind of delivery. You’ve got to let your body do all those things by itself without letting your mind take control’ (as quoted in Christensen et al. 2016: 37). The view of expert skilled performance that Sangakarra’s comments capture – one on which it is ‘mindless’, unreflective and largely automatic – has been standard in the psychology of skill for several decades, going at least as far back as the work of Fitts and Posner (1967) and the Dreyfuses in the 1980s. But in recent years, the standard view has been challenged by numerous theorists on the grounds that it fails to adequately account for the mindful, reflective and cognitively controlled aspects of skilled performance (Bermúdez 2017, Christensen et al. 2016, Fridland 2014, 2019, Montero 2016, Pacherie and Mylopoulos 2021, Shepherd 2019, Toner and Moran 2015).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".