Bibliographic record
Abstract
Abstract Environmental governance has the potential to be significantly transformed by Smart Environments technologies, e.g., technologies that enabled enhanced environmental monitoring and analytic procedures via combinations of information and communication technologies (ICT), conventional monitoring approaches (e.g., remote sensing), and Internet of Things (IoT) applications (e.g., Environmental Sensor Networks (ESNs)). This chapter offers an update of a 2018 paper that assessed these developments through the term “Smart Earth,” and which likewise engaged the potential implications and pitfalls of new digital technologies for environmental governance. Here, we offer a meta-review of research on what we now call “Smart Environments,” ranging from ecological informatics to the digital humanities. We pair this with a critical perspective on pathways for evolution in environmental governance frameworks, exploring five key Smart Environments issues relevant to environmental governance: data, real-time regulation, predictive management, open source, and citizen sensing. We conclude with suggestions for future research directions and transdisciplinary conversations about environmental governance in a Smart Environments world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".