Why we must question the militarisation of conservation
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
Concerns about poaching and trafficking have led conservationists to seek urgent responses to tackle the impact on wildlife. One possible solution is the militarisation of conservation, which holds potentially far-reaching consequences. It is important to engage critically with the militarisation of conservation, including identifying and reflecting on the problems it produces for wildlife, for people living with wildlife and for those tasked with implementing militarised strategies. This Perspectives piece is a first step towards synthesising the main themes in emerging critiques of militarised conservation. We identify five major themes: first, the importance of understanding how poaching is defined; second, understanding the ways that local communities experience militarised conservation; third, the experiences of rangers; fourth, how the militarisation of conservation can contribute to violence where conservation operates in the context of armed conflict; and finally how it fits in with and reflects wider political economic dynamics. Ultimately, we suggest that failure to engage more critically with militarisation risks making things worse for the people involved and lead to poor conservation outcomes in the long run.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it