How Can the Organizing Work Involved in the Joint Regulation of Lean Projects Promote an Enabling Organization and Occupational Health?
Bibliographic record
Abstract
The objective of this article is, through an empirical study, to further understanding of the actions and decisions taken in the context of Lean implementation projects carried out under joint regulation (Lévesque and Murray, 1998) agreements. We, therefore, attempt to identify factors that may facilitate the organizing work involved in joint regulation of Lean projects to allow workers to develop a broader range of health-minded work methods and habits. Our assumption is that factors which influence joint regulation, such as the union’s capacity for action, management’s attitude and the purpose of the change, also influence the occupational health outcomes of Lean projects. We believe that the organizing work involved in joint regulation (actions and decisions) has an impact on these factors and influences the occupational health outcomes. Our research question is therefore this: What are the actions and decisions involved in joint regulation of Lean implementation projects that lead to closer correspondence with enabling organization criteria? This empirical study was exploratory in nature and had a multiple case study design. Two cases of lean projects were documented through eight individual interviews and the collection of documents. The main results indicate that, while joint regulation appears essential in terms of meeting enabling organization criteria, it alone is insufficient to explain the health effects of Lean projects. All stakeholders need to define the project goals, modes of assessment and management rules, both cooperatively and transparently, and through their involvement in decisions regarding all processes.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".