The promise of posthumanism in wildlife ecotourism: a set of case studies of veterinarians’ role at wildlife rehabilitation centers in Costa Rica
Bibliographic record
Abstract
This study explored wildlife rehabilitation centers (WRCs) in Costa Rica as a potential posthumanist model for wildlife ecotourism. Posthumanism attends to the rights, welfare, and agency of nonhumans to depart from a conservation biology ethos that focuses on the species-level, to consider all particular individual animals in wildlife tourist attractions (WTAs). A team of 16 US-based researchers composed of faculty, a wildlife rehabilitation professional, and 12 university students conducted a 16-day pilot study to understand the context of wildlife rehabilitation, veterinarian practices, and ecotourism operations at three WRCs and a veterinary teaching hospital. Three rehabilitation centers are rated using a posthuman multispecies livelihoods framework. Ethnographic insights highlight practical challenges in operating rehabilitation centers and also the ethical challenges in promoting individual rights, welfare, and agency. Findings suggest that the level of treatment toward each individual animal in WRCs and ecotourism sites differs, based on the actions and beliefs of human actors who hold power over nonhumans. A major unforeseen ethical dilemma arose during the study concerning the treatment of prey species, which foregrounds the need for future research on this topic. By attending to ethical beliefs in WTAs, WRCs show their potential as a pathway for posthumanist ecotourism.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".