Bibliographic record
Abstract
This article presents textual evidence which shows some of the ways in which green business corporations and environmental NGOs represent the natural landscape and their relationship with it. It reviews the origin and development of stakeholder dialogue and questions to what extent such dialogue can contribute to a process of corporate change. It shows how the corporations use different language to represent nature than the NGOs and provides evidence suggesting that the green corporations understand their relationship with the natural landscape differently. NGOs that wish to speak up for the natural landscape, face a rhetorical dilemma which has an important implication for their practice. Either they can enter into a stakeholder dialogue with business and risk becoming a party to the exploitive management of nature, or they can refrain from entering into a dialogue and risk becoming marginalised.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.014 | 0.036 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".