Bibliographic record
Abstract
Job crafting is a self-management process of personalized job re-design that involves motives that can take the form of needs, dispositional tendencies, motivational states, and contextual demands/resources, which can be interpreted and internalized by job crafters into goals to be achieved or problems to be solved. Despite the importance that goals have within job crafting processes, research providing an integrative typology would help to specify the various functions and orientations of people’s job crafting goals. Thus, the paper uses qualitative and quantitative methods to develop an approach/avoidance typology of functional job crafting goals and test relevant correlates. Qualitative results show that job crafting goals can be considered according to both function (i.e., performance/development/well-being functions) and orientation (i.e., approach/avoidance orientations). Quantitative results from two additional studies then provide support for this dimensional structure and show that goals specified according to their function and orientation can relate to relevant predictors (autonomy and proactive personality), approach/avoidance job crafting behaviors, and co-worker-rated outcomes (performance and engagement). These findings complement our understanding of different types of job crafting activities by suggesting that people’s different reasons for crafting their jobs can explain new variance in job crafting behaviors and outcomes, helping to inform both self-managed job crafting and organizational interventions.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".