The Ontario Language, Social Studies, and Environmental Curriculum and Perceptions of the Relationship Between Human and Nonhuman Animals: A Collaborative Action Research Study
Bibliographic record
Abstract
This dissertation investigates the dynamics of human-animal relationships within Ontario’s language, social studies, and environmental curricula. Using a qualitative approach, this collaborative action research (CAR) case study involves interviews with students and teachers who recount personal experiences with animals, inside and outside of the classroom, comment on pedagogy related to the treatment of animals, and attempt to create animal-centred lessons. Drawing from my background as an educator and animal enthusiast, this analysis is informed by a critical discourse analysis of both policy documents and case study interview data. The primary objective of the research was to identify effective pedagogical strategies that promote an intrinsic appreciation for nonhuman life. To support my research agenda, I use two primary conceptual frameworks—critical animal studies (Matsuoka & Sorenson, 2018; Nibert, 2014; Nocella, 2011; Taylor & Twine, 2014) and an engaged policy and practices perspective (Davis, 2014; Davis & Phyak, 2017; Ricento & Hornberger, 1996; Schecter et al., 2014)—and critical discourse analysis as an analytic tool. The findings reveal differing degrees of consciousness and moral responsibility towards animal welfare and highlight the need to revise educational policies and approaches. Citizenship education is identified as a portal through which the development of a higher moral consciousness with regard to the appreciation of nonhuman animals can be fostered. As well, policy revisions should be implemented within the Ontario curriculum and teacher training programs to ensure that educators possess the knowledge and skills to effectively teach the importance of nonhuman animal life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".