Where Are We on the Animal Welfare Map? Using GIS to Assess Stakeholder Diversity and Inclusion
Bibliographic record
Abstract
Inequities exist in all facets of society, and animal welfare organizations (AWOs) and their communities are no exception. These organizations interface with multiple stakeholder groups. An active analysis of stakeholder groups to identify under-served areas and communities has not been performed. Using stakeholder data from Toronto Humane Society (THS) from 2015-2019, this study performed a retrospective spatial analysis to identify well served and under-served geographic areas for adopters, surrenders, public veterinary service (PVS) clients, volunteers and foster parents, using Hot Spot analysis. Correlation analysis was performed to determine whether the spatial distribution of the groups correlated with the four socioeconomic metrics of the 2016 Ontario Marginalization Index (residential instability, material deprivation, dependency, and ethnic concentration), and a metric representing the distribution of Indigenous residents. For each stakeholder group, there were well served areas, typically in central Toronto where THS is located, and under-served areas, typically in the north-west and north-east corners of Toronto and in the surrounding cities of the Greater Toronto Area. The area served by THS PVS extended further north than the other hot spot areas. The number of adopters increased as the residential instability metric increased, whereas the number of adopters decreased as the ethnic concentration metric increased. The rate of surrenders increased as the Indigenous metric increased. Public Veterinary Service clients increased as the residential instability, material deprivation, and Indigenous metrics increased. One of the primary limitations of this study was the confounding factor of distance from THS. Individuals living further from THS are less likely to utilize its services, particularly if there is another accessible AWO nearby, and therefore may appear to reflect an under-served population that may not truly be under-served. A regional approach would help to overcome this limitation. The results provide useful insights into stakeholder engagement and provide a foundation for analysis of more targeted areas, as well as for strategies to reach under-served demographics. Similar analyses by other AWOs would be helpful to address inequities in a larger geographic area. Animal welfare organizations can improve program effectiveness by adding data analytics skills to the more traditional skills associated with this sector.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".