Doing Knowledge Transfer: Engaging Management and Labor with Research on Employee Health and Safety
Bibliographic record
Abstract
In workplace health interventions, engaging management and union decision makers is considered important for the success of the project, yet little research has described the process of making this happen. A case study of a knowledge-transfer process is presented to describe the practices and processes adopted by a knowledge broker who engaged workplace parties in discussions on research on physical and psychosocial factors important for employee health. The process included one-on-one interactions between the knowledge broker and individuals to explain the research, to build trust and credibility, and to explore the applicability of the research to their work (sense making). It also included facilitated group sessions, where the groups explored how the research could solve problems within the workplace (social construction of knowledge). The workplace context offered multiple opportunities that helped and hindered the flow of research. Nevertheless, this intense, sustained, knowledge-transfer intervention noted conceptual, structural, and political knowledge use.
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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 | 0.046 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".