Rights and Representation: Media Narratives about Disabled People and Their Service Animals in Canadian Print News
Bibliographic record
Abstract
Canadian news coverage is reflecting and shaping an evolution of thought about how we must publicly account for animals’ roles in the disability rights movement. Through a textual analysis of 26 news media articles published between 2012 and 2017, this research demonstrates that the media play a key role in reporting on discrimination, yet media narratives about service animals and their owners too often fail to capture the complexity of policies and laws that govern their lives. In Canada, there is widespread public confusion about the rights of disabled people and their service animals. This incertitude is relevant to both disability and animal oppression. This research identifies nine frames within the media narratives, as well as evaluating perspectives from critical animal studies in the news articles. These frames, which emerge in the media reports, in their descriptions of human and (less often) animal rights, illustrate public confusion surrounding these rights. The confusion is inevitable given the many laws in Canada that govern service animals. Thus, to give context to the news coverage, this article also surveys the legal protections for disabled people who use service animals in Canada, and suggests that until the news media understand the legalities surrounding service animals, they will not be well equipped to fulfil their role of informing the public. This is a lost opportunity in light of the media’s potential role as a pivotal tool to educate the public about disability and animal rights.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.026 | 0.022 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".