Interrogating Technology‐led Experiments in Sustainability Governance
Bibliographic record
Abstract
Solutions to global sustainability challenges are increasingly technology-intensive. Yet, technologies are neither developed nor applied to governance problems in a socio-political vacuum. Despite aspirations to provide novel solutions to current sustainability governance challenges, many technology-centred projects, pilots and plans remain implicated in longer-standing global governance trends shaping the possibilities for success in often under-recognized ways. This article identifies three overlapping contexts within which technology-led efforts to address sustainability challenges are evolving, highlighting the growing roles of: (1) private actors; (2) experimentalism; and (3) informality. The confluence of these interconnected trends illuminates an important yet often under-recognized paradox: that the use of technology in multi-stakeholder initiatives tends to reduce rather than expand the set of actors, enhancing instead of reducing challenges to participation and transparency, and reinforcing rather than transforming existing forms of power relations. Without recognizing and attempting to address these limits, technology-led multi-stakeholder initiatives will remain less effective in addressing the complexity and uncertainty surrounding global sustainability governance. We provide pathways for interrogating the ways that novel technologies are being harnessed to address long-standing global sustainability issues in manners that foreground key ethical, social and political considerations and the contexts in which they are evolving.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".