The impact of operator involvement in pollution reduction: case studies in Canadian chemical companies
Bibliographic record
Abstract
Abstract In contrast to most environmental engineering processes, the effectiveness of employee involvement in pollution reduction seems uncertain, ambiguous and far from being clearly established. For companies whose environmental policies have long rested on technical investments, this uncertainty raises several essential questions, namely ‘what is the true effectiveness of this approach?’, ‘to what degree does employee involvement, most specifically operator involvement, make it possible to significantly and measurably reduce environmental impacts?’ and ‘what type of change could this induce in company operations?’. This article proposes answers to these questions based on an empirical study of the preventive and behavioural aspects of environmental management in the Canadian chemical industry. Conducted in three chemical factories from the Montreal region, the case studies show that significant results, often exceeding managers' expectations, could be obtained through the operators' involvement. However, these results and the precise organizational changes that caused them were difficult to identify, measure and foresee. Copyright © 2005 John Wiley & Sons, Ltd and ERP Environment.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".