Transformational leadership and employee safety performance: A within-person, between-jobs design.
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
We investigated the extent to which the safety performance (i.e., self-reported safety compliance and safety participation) of employees with 2 jobs was predicted by their respective supervisors' transformational leadership behaviors. We compared 2 within-person models: a context-specific model (i.e., transformational leadership experienced by employees in 1 context related to those same employees' safety performance only in that context) and a context-spillover model (i.e., transformational leadership experienced by employees in 1 context related to those same employees' safety performance in the same and other contexts). Our sample comprised 159 "moonlighters" (73 men, 86 women): employees who simultaneously hold 2 different jobs, each with a different supervisor, providing within-person data on the influence of different supervisors on employee safety performance across 2 job contexts. Having controlled for individual differences (negative affectivity and conscientiousness) and work characteristics (e.g., hours worked and length of relationship with supervisor), the context-specific model provided the best fit to the data among alternative nested models. Implications for the role of transformational leadership in promoting workplace safety are discussed.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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