A multi‐rater assessment of organizational commitment: are self‐report measures biased?
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
Abstract Most investigations of organizational commitment have been conducted using self‐report measures, however, the veracity of self‐reports is often questioned. In a sample of 79 public‐sector administrative staff, we assessed two types of organizational commitment (affective and continuance) from the perspective of three different sources of raters (self, peer, and supervisor) to test three explanations of the factors influencing self‐report measures (observational opportunities, simple defensiveness, and moderated defensiveness). The pattern of correlations among the measures, analysed using the composite direct product multitrait–multirater approach, suggested that self‐report commitment measures are affected mainly by observations or experiences of the self‐reporter rather than by systematic bias related to defensive responding. This increases our confidence that scores from self‐report measures of affective and continuance commitment are veridical. Further, self‐ and peer‐based measures of commitment were largely redundant in the prediction of a job‐performance criterion whereas supervisory measures added unique predictive variance. Implications are discussed. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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