Conceptual framework for increasing legitimacy and trust of sustainability governance
Bibliographic record
Abstract
While the quantity of sustainability governance initiatives and systems has increased dramatically, crises persist over whether specific governance systems can be trusted as legitimate regulators of the sustainability of economic activities. This paper focuses on conceptual tools to improve our understanding of these crises as well as the facilitating factors and barriers for sustainability governance to play a role in transitioning to profoundly more sustainable societies than those that currently exist. Bioenergy is used throughout the paper as an example to aid contextually in understanding the theoretical and abstract arguments. We first define eight premises upon which our argumentation is developed. We then define sustainability, sustainability transition, legitimacy, and trust as a premise for obtaining effectiveness in communication and minimising risks associated with misunderstanding key terms. We proceed to examine the literature on "good governance" in order to reflect upon what defines "good sustainability governance" and what makes governance systems successful in achieving their goals. We propose input, output, and throughput legitimacy as three principles constituting "good" sustainability governance and propose associated open-ended criteria as a basis for developing operational standards for assessing the quality of a sustainability governance system or complex. As sustainability governance systems must develop to remain relevant, we also suggest an adaptive governance model, where continuous re-evaluation of the sustainability governance system design supports the system in remaining "good" in conditions that are complex and dynamic. Finally, we pull from the literature in a broad range of sciences to propose a conceptual "governance research framework" that aims to facilitate an integrated understanding of how the design of sustainability governance systems influences the legitimacy and trust granted to them by relevant actors. The framework is intended to enhance the adaptive features of sustainability governance systems so as to allow the identification of the causes of existing and emerging sustainability governance crises and finding solutions to them. Knowledge generated from its use may form a basis for providing policy recommendations on how to practically solve complex legitimacy and trust crises related to sustainability governance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13705-021-00280-x.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".