Environmental Management Systems (EMS) and learning in small and medium size establishments (SME) : a case study from the beverage industry
Bibliographic record
Abstract
For businesses that are internally motivated to incorporate environmental management into daily practice, an environmental management system (EMS) is an effective tool to address environmental impacts. Yet, certification to formal EMS standards such as ISO 14001 may pose challenges for the unique needs of a small and medium-sized establishment (SME) such as Company Y, who seeks systematized environmental management while maintaining flexibility and openness. The researcher explores the proposition that EMS implementation and performance of an SME in the position of Company Y can be optimized by incorporating key tenets of Organizational Learning theory (OLT) into decision making and operations. Primary questioning, observation and literature research are used to characterize Company Y’s environmental decision-making and communication structure. For growing SMEs that are not comfortable with the formal requirements of third party EMS certification, this thesis suggests that OLT can be an effective approach to integrate environmental management into their business.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".