Biodiversity Compliance for Businesses : An investigation into the regulatory implications of biodiversity on business
Bibliographic record
Abstract
This thesis investigates the intricate relationship between biodiversity and businesses within the regulatory realm. The study will specifically look into new compliances imposed by international organisations such as The Kunming-Montreal Global Framework for Biodiversity, the Biodiversity strategy for 2030 in the European Green Deal and the upcoming Corporate Sustainability Reporting Directive. A meticulous literature review and thorough semi-constructed interviews were conducted and combined in the analysis. The research applies an Institutional theory framework and aims to seek the implications of institutional pressures. Furthermore, findings suggest collaboration and the use of external actors such as consultants are of importance in adaption. Challenges found highlight the need for standardised measurements, assistance to handle data and the lack of economic incentives. This study provides insightful findings in understanding how the current and upcoming biodiversity compliance affects businesses in Europe. Future research should focus on specific industry sectors, regions, and business sizes, investigate the dialogue between businesses and regulators, explore how businesses impact their external environment, and examine how they navigate new and unclear regulations.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".