The Importance of Being Expert: A Multi-Method Approach to Modeling Expert Cognition in Naturalistic Environments
Bibliographic record
Abstract
The following work aims to contribute to the study of experts and expertise.It is concerned with three principle questions.First, what is expertise?Second, which methodologies can be usefully applied to the study of expertise as it operates in naturalistic contexts?Third, how can the scientific community evaluate and refine these methodologies, as well as develop new ones?These questions are addressed using three frameworks: the Universal Architecture of Expertise, Methodological Pipelining, and Lakatosian Analysis.Three studies of expert cognition are presented, each of which uses a different methodology.The first study presents a novel method for building macro cognitive models of experts, and applies this method to an analysis of individuals playing a fastpaced video game (Gears of War 3).The second study is a communication analysis of teams playing Gears of War 3 and Counter Strike: Global Offensive, with a focus on communication relating to coordination and interruption handling.The third study is a Python ACT-R model of team coordination using a simplified simulation environment based on Gears of War 3 gameplay.Taken together, these three studies are intended as a demonstration of a research programme built upon the aforementioned three frameworks.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".