Well-being and human-animal interactions in schools: The case of "Dog Daycare Co-Op"
Bibliographic record
Abstract
This paper draws on Martha Nussbaum’s account of the nature of human well-being to explore the role of animals in formal education settings. Nussbaum equates well-being with human flourishing, and argues that people live well when engaged in essential functions that are particular capabilities, each a necessary but insufficient contributor to well-being. One of these capabilities is the ability to “to have concern for and live with other animals, plants and the environment.” Yet, this condition of well-being remains largely unexplored among in education. In recent years, the benefits of human-animal interaction in education settings has been researched and discussed in the social sciences, particularly the use of dogs to aid reluctant readers in literacy development, and the use of therapy dogs in universities during final examination blocks. This paper presents findings of one particular research project of the effects of a unique, Canadian school-based cooperative education program, “Under One Woof,” in which students work with animals. Based on interviews, students’ own stories of the impact of animal interaction – particularly in light of other challenges they faced academically and socially – appear to support other empirical accounts of positive effects of animals in education settings, and offer insight into the nature and effects of human-animal interaction as an element of well-being.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.041 | 0.043 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".