Does Explicit Categorization Taxonomy Facilitate Performing Goal-Directed Task Analysis?
Bibliographic record
Abstract
Situation awareness (SA) is an important factor that affects the performance of operators who work in complex environments. SA is defined as the perception of elements in an environment (level 1), understanding their meaning (level 2), and the projection of their state into the future (level 3). The first step to assess SA is identifying its requirements, for which goal-directed task analysis (GDTA) is the recommended technique. GDTA is a type of cognitive task analysis that focuses on the goals that a human operator must achieve, and the information required to accomplish them. The result of GDTA is a list of information (SA elements) that are categorized into the three SA levels. However, GDTA-based studies typically categorize SA elements into the three SA levels without stating their categorization rules. Therefore, this article proposes a taxonomy to categorize SA elements obtained via GDTA into SA levels. First, we present the results of a systematic literature review (N = 87) to gain insight into how analysts apply their classification criteria. Then, we propose a categorization taxonomy based on the ISO 15939 standard. To validate the proposed taxonomy, we analyze a subset of GDTA results in two cases, and the results of the analysis show that the proposed categorization rules are applicable to the two analyzed cases.
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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 | 0.037 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".