« Green alliances » between businesses and environmental groups : reconciliation or reciprocal manipulation ? [Les « alliances vertes » entre les entreprises et les associations de protection de l’environnement : une réelle réconciliation ou une « instrumentalisation » réciproque ?]
Bibliographic record
Abstract
« Green alliances » between businesses and environmental groups are based on mutual benefits and include several forms and degrees of cooperation. Despite promises of win-win results, several adverse effects could occur. We define and characterize green alliances. We study the main drivers of firms and activists participation in these partnerships and explore the risks of « reciprocal manipulation ». In addition, we briefly develop two cases of green alliances, the first one between Pollution Probe and the Canadian retailer Loblaws, and the second one between the WWF and the world leader in building materials Lafarge. Lastly, some policy considerations are stressed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".