Assessing job crafting competencies to predict tradeoffs between competing outcomes
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 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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it