The Srk Inventory: A Tool for Structuring and Capturing a Worker Competencies Analysis
Bibliographic record
Abstract
Worker Competencies Analysis (WCA) is the fifth and final phase of the Cognitive Work Analysis (CWA) framework. Unlike the earlier four phases, there is a dearth of published work illustrating how WCA is conducted within the context of CWA. The lack of concrete examples of the application of WCA has both practical and pedagogical ramifications, making it difficult to perform and understand this phase of analysis. This paper attempts to address this gap. Following a review of the CWA framework, WCA is introduced with the Skill, Rules, and Knowledge (SRK) taxonomy. Then, a methodological tool for structuring and capturing the execution of WCA—the SRK Inventory—is presented. Finally, a practical application of the SRK Inventory to a TRACON microworld is discussed. This paper is intended to serve as a resource to future CWA practitioners and researchers, and to stimulate discussion of methods and tools for better supporting WCA activities.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".