Enacting ecological and collaborative rationality through multiparty collaboration – a case of innovation in governance
Bibliographic record
Abstract
The article presents the case study of a partnership between a metallurgy company and an NGO concerned with environmental protection. The partnership constituted an attempt to reconcile the firm's economic objectives with those of the citizens who lived in the area on which it had an ecological impact. Driven by high ideals, the multistakeholder partnerships were an innovation inspired by the ideal speech situation theory and a focus on learning and innovation. The partnership seemingly created an arena defined by norms of 'disinterested rationality' with an objective of innovating and progressing toward sustainable development. The partnership had only a marginal influence on the firm's activities, which were mainly determined by market forces and economic logic. The article concludes with a rather critical perspective on the outcomes of the case in terms of learning, innovation and change, with a theoretical lens inspired by theories on learning, legitimacy and power. The article contributes to the understanding and definition of legitimacy in a polyphonic context, where different views coexist or confront. Legitimacy is neither an outside nor static institutional feature, but rather resembles a kaleidoscope of perceptions that are defined, temporarily granted and redefined through discursive interactions. In such a context, moral arguments are confronted with other moral arguments while actors redefine their knowledge and cognitive frameworks. Practical recommendations are formulated for the convenors of multistakeholders partnerships, activist groups and firms.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".