Editorial: Governance for Sustainable Development in the Face of Ambivalence, Uncertainty and Distributed Power: an Introduction
Bibliographic record
Abstract
Three fundamental observations on the contemporary debate on governance and steering for sustainable development are outlined. First, sustainable development as a highly normative, yet extremely vague concept inescapably raises issues of governance and political steering. Second, the many contributions, approaching sustainability governance from multiple angles, have in common that they assume sustainability goals to a certain extent as given. Third, sustainability poses specific challenges to governance that are different from other policy fields. In this context, exiting contributions highlight issues of complexity, uncertainty or ambivalence, albeit in a rather cursory manner. Against this background, a specific approach is introduced, exploring the complexities that arise from limits to rational steering in three dimensions: Sustainability goals are ambivalent in that they are subject to controversies based on heterogeneous perceptions, values and interests of individuals and societal groups. Moreover, the knowledge of the complex dynamics involving society, technology and nature typically remains highly uncertain. Finally, the power to shape structural change in society and technology is distributed across a multitude of actors and societal subsystems. The article concludes by outlining the structure of the present collection of papers and by summarising each contribution.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".