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Record W2319906952 · doi:10.1177/154193120605000362

The Srk Inventory: A Tool for Structuring and Capturing a Worker Competencies Analysis

2006· article· en· W2319906952 on OpenAlex
Ryan Kilgore, Olivier St-Cyr

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2006
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsStructuringComputer scienceContext (archaeology)Work (physics)Taxonomy (biology)Knowledge managementResource (disambiguation)Process managementManagement scienceEngineeringBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.269
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it