Corporate Biodiversity Management through Certifiable Standards
Bibliographic record
Abstract
Abstract This article analyzes the motivations, internalization challenges and outcomes of implementing certifiable standards for corporate biodiversity management. For this purpose, a qualitative study based on interviews with 39 environmental managers, auditors, consultants and other experts in the field was conducted. The findings show that the adoption of new standards for biodiversity management is essentially driven by the need to improve the social acceptability of activities that can have a significant impact on natural habitats. The possible benefits of certification, particularly in terms of stakeholder relationships, and the difficulty of measuring the intangible aspects of biodiversity issues are also discussed. The study contributes to the emerging literature on organizational biodiversity management and to the debates on the symbolic versus substantial adoption of certifiable environmental standards. Managerial implications for organizations interested in biodiversity management are also discussed. Copyright © 2017 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 | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".