Siloes and silences: a review and reflection on environmental law and digital technology scholarship
Bibliographic record
Abstract
This article explores conversations on environmental law and next-generation digital technologies that are happening and not happening. Environmental law’s intersections with digital technologies, such as big data, artificial intelligence (AI), and blockchain remain only selectively examined. Based on a scoping review of the scholarly literature, the article tentatively categorises existing scholarship into four discrete groups, namely evaluative mapping, systemic integration, convivial interdependence and technosolutionism. This view of the scholarship focuses on the who, what, and where shaping current knowledge practices and their dissemination – the siloes. We also draw on our own research experiences and broader reflections to identify the silences in environmental law scholarship’s engagement with digital technologies. Our review and reflection suggests that environmental law’s engagement with technology may be developing as a series of niche, and isolated, conversations. The article concludes by presenting new opportunities for future research about environmental law’s interactions with technology.
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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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".