Bibliographic record
Abstract
Begun as a eulogy to the author’s late companion dog, Tashi, this paper presents a “petagogy” of emotions as a strategy to enhance human apprehension of our impact on the greater-than-human-world. Occupying the null curriculum of modern education, both emotions and non-human animals have been ill-served by science and the disciplinary organization of modern formal education. Drawing on ecological perspectives of symbiosis and mutual interdependence and a narrative inquiry into her relations with Tashi, the author considers six areas for the education of emotions through petagogy: mutual social bonding, self-regulating negative impulses, enhancing positive feelings, developing empathy, communicating to cooperate, and responding to suffering and death. With these foci, inquiry into animal-human companionship offers reciprocal opportunities to deepen and develop our emotional lives and empathic capacities across species. Resume D’abord destine a faire l’eloge de Tashi, defunt compagnon canin de l’auteure, cet article presente une « zoopedagogie » des emotions a titre de strategie d’amelioration de la comprehension humaine de ses propres effets au-dela de l’humanite. Occupant le curriculum caduc de l’education moderne, les emotions comme les animaux non humains ont ete mal servis par la science et l’organisation disciplinaire de l’education systematique moderne. S’inspirant des perspectives ecologiques de la symbiose et de la dependance mutuelle, ainsi que d’une introspection narrative sur ses relations avec Tashi, l’auteure se penche sur six domaines d’enseignement des emotions par la zoopedagogie : le tissage de liens sociaux, le controle des impulsions negatives, la culture d’emotions positives, l’empathie, la communication visant la collaboration et la reaction devant la souffrance et la mort. Par ces elements centraux, l’etude de la camaraderie animalhumain offre des occasions reciproques d’approfondir les vies emotionnelles et les facultes empathiques chez toutes les especes.
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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 | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".