Defining expertise: A taxonomy for researchers in skill acquisition and expertise
Bibliographic record
Abstract
How researchers identify and define levels of skill (e.g., what is an ‘expert’?) is surprisingly inconsistent across studies in expertise research. This lack of clarity regarding the definitions used in skill acquisition has obvious implications for scientists working in this area. More specifically, the accuracy and appropriateness of definitions for different levels of skill are central to the study of expert performance, from both a theoretical and methodological perspective. This presentation will summarize general approaches used in the past, highlight the need for clear delineations between groups of performers at different levels of skill and propose a taxonomy of sport-skill classification that could aid researchers in this area. This taxonomy is designed to provide a general system for categorizing skill across sport and delineates the various stages in skill acquisition. The taxonomy starts with early phases of skill development beginning with naïve (no skill) and novices (limited skill) and moves to transitional phases of development (basic, intermediate and advanced levels of skill) before reaching peak levels of skill (i.e., expert and eminence). Although there are limitations to any taxonomy of skill, continued attention and discussion of these issues are necessary to ensure that emerging evidence can be optimally integrated into the knowledge base regarding acquisition of sport skill.
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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 | 0.072 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.018 | 0.033 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".