TAKE THIS JOB AND LOVE IT: A MODEL OF SUPPORT, JOB SATISFACTION, AND AFFECTIVE COMMITMENT AMONG MANAGERS OF VOLUNTEERS
Bibliographic record
Abstract
Several job‐related and organizational features make the work of community‐based paid managers of volunteers distinctly different from conventional management practice. Based on self‐verification (Swann & Brown, 1990) and exchange (Blau, 1964) theories, we tested a multidimensional measurement model of support specific to these managers. The dimensions include support gained from their coworkers, volunteers, and supervisors, and from the prosocial, value‐expressive nature of the work. This model of support predicted the managers’ job satisfaction, which mediated the relationship between support and affective commitment, with value‐expressive work being the strongest predictor. Both the measurement model of support and the structural predictive model were found to be invariant across managers with greater and less than 10 years of work experience. The findings spotlight the importance of sources of workplace support that shore up employees’ valued identities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".