Approaches to learning at work and workplace climate
Bibliographic record
Abstract
Three studies are reported concerning employees' approaches to learning at work and their perceptions of the workplace environment. Based on prior research with university students, two questionnaires were devised, the Approaches to Work Questionnaire (AWQ) and the Workplace Climate Questionnaire (WCQ). In Studies 1 and 2, these questionnaires were administered to two different samples of employees, and the factor structure of the questionnaires was explored. In Study 3, the two data sets were combined, and a random half of it was used to develop reduced sets of items that addressed selected factors for each of the questionnaires. The other half of the data was used to test the scales developed. For the AWQ, three factors are proposed: deep, surface‐rational, and surface‐disorganised. The first of these is consistent with the student learning literature, but the other two represent a division of a unitary surface factor. The three components of the WCQ are good supervision, choice‐independence, and workload. Correlations between scales indicated that the deep approach is positively associated with good supervision and choice‐independence, whereas the surface‐disorganised approach is negatively associated with these two constructs and positively associated with workload. Surface‐rational is negatively, though less strongly associated with choice‐independence. Suggestions are presented for use of these instruments in future research and practice.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".