Ethical ecosurveillance: Mitigating the potential impacts on humans of widespread environmental monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Ecosurveillance has proliferated in recent years, generating vast amounts of data on the natural environment. Ecosurveillance also has significant potential impacts on humans; therefore, researchers and policymakers need new conceptual tools to anticipate and mitigate any negative effects. Surveillance studies is an interdisciplinary field in the social sciences, providing a number of insights and practical lessons for predicting and managing the complex impacts (positive and negative, intended and unintended) of surveillance tools and practices. We draw on surveillance studies literature to propose two tools to guide designers and practitioners of ecosurveillance—a ‘red flag checklist’ to anticipate potential problems, and a ‘considerations guide’ to inform design decisions across a wide range of ecosurveillance systems. These tools will help ensure that the coming era of ecosurveillance is guided by responsible and ethical practices towards wildlife and humans alike, while also realizing the potential of these technologies for improving environmental outcomes. Read the free Plain Language Summary for this article on the Journal blog.
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.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it