Approaches to learning at work and workplace climate
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it