Bibliographic record
Abstract
The objective of this paper is to highlight animal stakeholders, evidenced-based best practices, care ethics, and compassion as essential components of sustainable wildlife tourism. These tenets stem from an animal geography lens, which is well-positioned for studies of animal-based tourism and transspecies caregiving. As a conceptual contribution, this paper presents a theory synthesis that ‘stays with the trouble’ of wildlife tourism and identifies ways to ‘bring animals in’. Our approach could be described as multispecies, critical, and socio-ecological. We argue that the trouble with wildlife tourism writ large includes nonhuman suffering and biodiversity loss, unethical and unevidenced practices, gaps in the knowledge of wildlife welfare, and limited engagement with animals as stakeholders. We then present four ways to ‘bring animals in’ as co-participants in wildlife tourism research and practice. This involves enfranchising animals as stakeholders in wildlife tourism, buttressed by ethics of care, best practices, and a commitment to improved outcomes along the conservation-welfare nexus. Finally, we consider the extent to which wildlife sanctuary tourism serves as a further problem or panacea that balances the conservation and welfare of wild animals. The result of our theory synthesis is the promotion of a more care-full and compassionate paradigm for wildlife tourism, which draws from diverse scholarships that contribute, conceptually and practically, to the underserved niches of wildlife welfare, rehabilitation, and sanctuary research.
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.
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 | 0.006 | 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 | 0.008 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".