How “situational” is judgment in situational judgment tests?
Bibliographic record
Abstract
Whereas situational judgment tests (SJTs) have traditionally been conceptualized as low-fidelity simulations with an emphasis on contextualized situation descriptions and context-dependent knowledge, a recent perspective views SJTs as measures of more general domain (context-independent) knowledge. In the current research, we contrasted these 2 perspectives in 3 studies by removing the situation descriptions (i.e., item stems) from SJTs. Across studies, the traditional contextualized SJT perspective was not supported for between 43% and 71% of the items because it did not make a significant difference whether the situation description was included or not for these items. These results were replicated across construct domains, samples, and response instructions. However, there was initial evidence that judgment in SJTs was more situational when (a) items measured job knowledge and skills and (b) response options denoted context-specific rules of action. Verbal protocol analyses confirmed that high scorers on SJTs without situation descriptions relied upon general rules about the effectiveness of the responses. Implications for SJT theory, research, and design are discussed.
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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 | 0.041 | 0.248 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".