A mixed-method analysis of the consistency of intake information reported by shelter staff upon owner surrender of dogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data collected by animal shelters can provide an overview of population numbers and recommendations for shelter management and community programming. While studies utilize data from shelter software, questions remain on whether such data are reliable. The objective of the online experiment was to determine the agreement in data input for surrender reason, breed, and color across shelter staff (n = 81) when presented with four complex narratives of fictional owners surrendering dogs. Additionally, we aimed to understand how staff select surrender reasons for data input through qualitative analysis. Out of 40 possible surrender reasons, the number of unique reasons selected for each scenario ranged from 12–16, suggesting a variety of possible data entries for the same surrender narrative. Agreement was also low for breed and color. Shelter staff described a variety of different methods of determining the surrender reason for input into shelter software, such as asking the owner for their most influential reason or inferring the underlying reason. Further research is required to understand how animal shelter data can be collected consistently in a way that can meaningfully inform shelter management decisions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it