Grounding drones in political ecology: understanding the complexities and power relations of drone use in conservation
Bibliographic record
Abstract
Rapidly evolving drone technologies are taking the conservation sector by storm. Although the technical and applied conservation literature tends to frame drones as autonomous, neutral technologies, we argue that neither drones nor their implications can be adequately understood unless they are grounded, conceptually and methodologically, in the context of broader societal structures that shape how drones and the data they produce are used. This article introduces the value of a political ecology framework to an interdisciplinary audience of biophysical and social scientists interested in the multiple possibilities and complications associated with conservation drones. Political ecology provides the tools for studying and critically engaging with drone use in conversation in ways that are politically engaged and attuned to power relations – historic and present, local and global – in a more-than-human world. In making this argument, we point to four conceptual tools in political ecology that offer a framework for unveiling the power relations and structures that surround drones in different contexts: political economy, territoriality, knowledge and expertise, and more-than-human relations. Using empirics from our work across Latin America (Colombia and Guatemala), Africa (Kenya, Tanzania, South Africa and Mozambique), and North America (the US and Canada), we illustrate the salience of this framework and demonstrate why evaluating what drones do in and for conservation requires first understanding the complex set of power relations that shape their use.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.065 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".