Principles for the socially responsible use of conservation monitoring technology and data
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 Wildlife conservation and research benefits enormously from automated and interconnected monitoring tools. Some of these tools, such as drones, remote cameras, and social media, can collect data on humans, either accidentally or deliberately. They can therefore be thought of as conservation surveillance technologies (CSTs). There is increasing evidence that CSTs, and the data they yield, can have both positive and negative impacts on people, raising ethical questions about how to use them responsibly. CST use may accelerate because of the COVID‐19 pandemic, adding urgency to addressing these ethical challenges. We propose a provisional set of principles for the responsible use of such tools and their data: (a) recognize and acknowledge CSTs can have social impacts; (b) deploy CSTs based on necessity and proportionality relative to the conservation problem; (c) evaluate all potential impacts of CSTs on people; (d) engage with and seek consent from people who may be observed and/or affected by CSTs; (e) build transparency and accountability into CST use; (f) respect peoples' rights and vulnerabilities; and (g) protect data in order to safeguard privacy. These principles require testing and could conceivably benefit conservation efforts, especially through inclusion of people likely to be affected by CSTs.
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 | 0.005 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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