Developing a measure of work uncertainty
Bibliographic record
Abstract
Uncertainty is a key contingency in the relationship between work characteristics and outcomes such as employee performance and well‐being. In this paper, we specify and test a self‐report measure of work uncertainty for use in any setting to facilitate research and decision making regarding the design of work. Using data collected from three diverse samples, analyses found support for a multi‐dimensional model that corresponds to resource, task, and input/output sources of uncertainty. The scales showed discriminant validity, and task uncertainty was found to moderate the relationship between job control and intrinsic job satisfaction in a form consistent with theoretical predictions. Practitioner Points The self‐report measure of work uncertainty may be used to evaluate existing work design and facilitate its redesign. As research demonstrates, it is critical that the level of job control afforded to employees is congruent with the level of uncertainty they experience. Failure to consider the role of uncertainty in linking job control to outcomes (e.g., performance, well‐being) can undermine work redesign investment.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".