Recognizing Zooeyia to Promote Companion Animal Welfare in Urban Bangladesh
Bibliographic record
Abstract
The One Health concept of zooeyia refers to the benefits of companion animals in human health and is gaining global research attention. This exploratory study aimed to understand contemporary experiences and perceptions of the social benefits and challenges of living with a companion animal in urban Bangladesh. Thirty-five qualitative interviews were conducted with companion animal owners (20), animal sellers (10), and livestock service department officers (5) from two major cities in Bangladesh, Dhaka and Khulna. Thematic analysis found that historically, animals had a utilitarian purpose, such as livestock for food and dogs for security. The role and perceptions of companion animals began to change for some around the turn of the century. Today, companion animal caretakers report social, psychological and physical health benefits from integrating companion animals into their lives. They also report that companion animal ownership can contribute to social problems due to the prevailing stigma against companion animals. This is rooted in the continued utilitarian role attached to companion animals by the majority of the Bangladesh population as well as religious-based non-acceptance. As a result, the Animal Welfare Act (2019) is not well implemented, posing a key concern for companion animal welfare. To tackle this, we propose various ways in which the emerging concept of zooeyia can help promote the welfare of companion animals by challenging the stigma associated with them in Bangladesh.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".