A Mixed-Methods Exploration Of Opportunities For Barriers And Bias During Off Site Animal Adoption Events
Bibliographic record
Abstract
Introduction: Adopters and animal shelter leaders are calling for higher equity in services provided by animal shelters to their communities. Specifically, restrictive pet adoption processes have been proposed as a threat to equitable service. The study had three specific goals: (1) to describe the basic characteristics of off-site adoption events, (2) to describe the adoption processes in use by shelters and to determine if there is any potential for unconscious bias in these processes, and (3) to determine if adoption practices differ depending on the characteristics of the animal shelter. Methods: A total of 484 participants reported on how they conduct adoption events and their adopter selection procedures. Characteristics of organisations and their adoption event procedures were described and compared across organisation size using Chi-squared tests. Qualitative responses to open-ended questions asking about screening procedures were analysed through inductive thematic analysis. Results: Commonly, adoption events were held weekly (n = 158, 33.1%) in locally owned stores (n = 259, 54.4%), with volunteers engaging directly with potential adopters (n = 423, 88.5%). Just under half responded that adopters could take pets straight home from the event (n = 229, 47.8%) and that the adoption process does not require a home visit (n = 276, 57.7%). Qualitative analysis revealed three major groups of themes: ‘methods of selection’, ‘information gathered by the animal shelter’, ‘information provided by the animal shelter’. The sub-theme of ‘Vibes’ (coded for 39.4% participants, n = 175) encompassed decision-making based on intuition or feeling when meeting or observing potential adopters face-to-face. Generally, larger organisations held adoption events more frequently, were more likely to let adopters take animals straight home, and did not require a home visit. However, both types of organisations showed a potential for implicit bias when selecting adopters. Conclusion: Our results suggest that procedures of adoption events in animal shelters vary across organisation, with some practices differing based on organisation size. Given that our results suggest possibility for implicit bias when screening adopters during adoption events, we conclude that there is a need for increased awareness, research, and training to address the issue of implicit bias in animal sheltering organisations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".