Understanding Factors That Influence Small Business Participation In Environmental Improvement Activities: A Study Of Businesses Involved With The Eco-Efficiency Centreâs Environmental And Energy Review Program.
Bibliographic record
Abstract
Small businesses have on the whole fallen behind in attempts to reduce environmental impact and have been noted to face particular challenges in undertaking and implementing environmental improvements in their operations. With this recognition, programs have been created to encourage businesses to reduce their environmental impact. Dalhousie University, in Halifax, Nova Scotia, Eco-Efficiency Centre (EEC), Environmental and Energy Review Program provides awareness and guides small businesses in methods to undertake environmental improvements. Examining a sample of businesses involved with the EEC program, the objective of this research is to understand what motivates micro and small-sized businesses to improve their environmental performance and explore the challenges they face in the process. By analysing the motivations and challenges to reducing environmental impact, it is anticipated this research may help programs and policy makers and better encourage businesses to undertake environmental improvements.
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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 | 0.000 | 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 | 0.000 |
| 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".