Assessing job crafting competencies to predict tradeoffs between competing outcomes
Bibliographic record
Abstract
Abstract We introduce the job crafting competency construct and apply it to predict tradeoffs between competing outcomes that are inherent in job crafting, like performance and well‐being or engagement and withdrawal. Job crafting competencies are the clusters of individual knowledge, skills, and abilities that are necessary to achieve personal objectives through effective job crafting problem‐solving. We create a framework of job crafting competencies consisting of comprehensive/simplistic heuristic information use and approach/avoidance problem‐solving skills. In Study 1, we operationalize competencies as profiles demonstrated through an aptitude‐oriented assessment that predicts differences in outcomes. Five distinct profiles emerged in a sample of 174 workers. The high‐volume analytic problem‐solving profile was associated with higher performance and strain, while the ambivalent acquiescence profile was associated with lower performance and strain. The practical problem‐solving profile minimized tradeoffs between performance and strain. Rapid problem‐solving and low‐volume analytic problem‐solving profiles were variants in between these other patterns. Study 2 used a survey of 323 workers to support the uniqueness of the five competencies, and their relationships with approach/avoidance job crafting, engagement, and withdrawal. The research identifies a new job crafting individual difference (job crafting competencies) to delineate outcomes and tradeoffs according to unique competency profiles.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".