Compliance and enforcement in a brave new (green) world: best practices and technologies for green governance
Bibliographic record
Abstract
International and transnational cooperation is needed to strengthen environmental governance initiatives with advanced technologies. In January 2023, Ontario Tech University hosted a symposium entitled Tech With a Green Governance Conscience: Exploring the Technology–Environmental Policy Nexus. Attendees spanned diverse disciplines, sectors, and countries, bringing unique and diverse perspectives to the technology–environmental policy nexus. Emergent themes arising from the symposium include the role of artificial intelligence in environmental governance, while eliminating the detrimental social impacts associated with these advanced technologies via algorithmic bias, misunderstanding, and unaccountability. The symposium explored the tech–society–ecology interface, such as the authoritarian intensification of digitalized environmental governance, “technocracy”, and the ethical implications of sacrificing democratic legitimacy in the face of imminent environmental destruction. Select participants (i.e., co-authors) at the symposium provided input on a preliminary framework, which led to this perspective article focused on the politics surrounding green governance in the 21st century. We conclude that while emerging technologies are being deployed to address grand environmental challenges such as climate change, biodiversity loss, and resource depletion, the use of these various technologies for progressive environmental policy development and enforcement requires co-productivist approaches to constructive technology assessments and embracing the concept of technologies of humility. This necessitates a space for dialogue, reflection, and deliberation on leading adaptive environmental governance in the face of power and politics, as we interrogate the putative neutrality of advanced technology and techno-solutionism.
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 | 0.082 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.087 |
| Scholarly communication | 0.033 | 0.035 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.012 | 0.011 |
| 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".