Teaching the Nonhuman Animal in Higher Education: Interdisciplinary Experiential Learning
Bibliographic record
Abstract
Abstract As human-animal studies ( HAS ) scholarship has grown and expanded over the past few decades, so have opportunities to bring nonhuman animals into higher education. This article presents an instructional design option for teaching the animal through interdisciplinary experiential learning. Interdisciplinary learning integrates multidisciplinary knowledge across a central theme while experiential learning encourages learners to move through a recursive process of experiencing, reflecting, thinking, and acting. The article also reflects on student learning outcomes based on a questionnaire survey conducted five years after the course completion. Preliminary insights reveal the transformative potential of this approach given students’ modified viewpoints, enhanced ethical sensitivity, enlarged horizons, and behavioral changes regarding animals. HAS scholars are encouraged to engage in animal-focused scholarship of teaching and learning in higher education by sharing instructional templates and scholarly research on HAS courses. Doing so will expand opportunities for students to appreciate, critically examine, and positively influence animal lives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".