Animal sheltering: A scoping literature review grounded in institutional ethnography
Bibliographic record
Abstract
A diverse research literature now exists on the animals, staff and organisations involved in animal sheltering. We reviewed this research through the lens of institutional ethnography, a method of inquiry that focuses on the actual work that people do within institutions. The main topics, identified through a larger ethnographic study of animal sheltering, were: (i) research about shelter staff and officers; (ii) the relinquishment of animals to shelters; and (iii) animals' length of stay in shelters. After reviewing the literature, we held focus groups with shelter personnel to explore how their work experiences are or are not represented in the research. The review showed that stress caused by performing euthanasia has attracted much research, but the decision-making that leads to euthanasia, which may involve multiple staff and potential conflict, has received little attention. Research on 'compassion fatigue' has also tended to focus on euthanasia but a granular description about the practical and emotional work that personnel undertake that generates such fatigue is missing. Published research on both relinquishment and length of stay is dominated by metrics (questionnaires) and often relies upon shelter records, despite their limitations. Less research has examined the actual work processes involved in managing relinquishment as well as monitoring and reducing animals' length of stay. Institutional ethnography's focus on people's work activities can provide a different and more nuanced understanding of what is happening in animal sheltering and how it might better serve the needs of the animals and staff.
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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 | 0.030 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.051 | 0.041 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".