Compassionate Conservation: Exploring the Lives of African Wild Dogs (Lycaon pictus) in Botswana
Bibliographic record
Abstract
This paper argues for a more compassionate conservation by positioning animals as subjects in research and scholarship. Compassionate conservation is a multidisciplinary field of study that broadly attends to the ethical dimensions of conservation by merging conservation biology and animal welfare science. However, animal geography is rarely discussed in the compassionate conservation scholarship despite sharing similar tenets. This paper argues that responsible anthropomorphism and animal geography concepts of animal subjectivity (lived experiences) and agency (capacity to act) positions African wild dogs (Lycaon pictus) as subjects in conservation research and scholarship. It merges biological research, public communication, and interview and participant observation data to present wild dogs as thinking, feeling, self-conscious animals with agency, and whose welfare is negatively affected in human-dominated landscapes in Botswana. This paper argues for more attention to be paid to animal subjectivity and agency to foster more compassionate relations with wildlife. It argues that positioning animals as subjects in research and scholarship is an ethical starting point for moving compassionate conservation forward. This ‘enriched’ scholarly approach moves us closer to appreciating the lives of wildlife and the complexity of their circumstances and experiences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".