MétaCan
Menu
Back to cohort
Record W2044542538 · doi:10.1163/15685306-12341353

The Political Landscape Surrounding Anti-Cruelty Legislation in Canada

2015· article· en· W2044542538 on OpenAlex
Antonio Robert Verbora

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSociety and Animals · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCrueltyLegislationCriminal codeLawPoliticsPolitical scienceGovernment (linguistics)NothingAnimal welfareCriminologyCriminal lawSociologyEcologyBiology

Abstract

fetched live from OpenAlex

In 1998, the federal government launched a consultation process, which pointed out that nothing significant had been done to change federal anti-cruelty laws in Canada since 1892. The consultation process concluded that among other concerns, outdated wording of the law has prevented the prosecution of many serious nonhuman animal abusers. Since 1999, there have been a number of failed amendments to the Criminal Code anti-cruelty provisions. The study examines the trajectory of the proposed changes since 1999 to the present, using official transcripts of Canadian parliamentary debates, and seeks to understand the politics of animal cruelty legislation in Canada. Using thematic analysis, this paper explores how resistance to the amendments is articulated and rationalized, as well as the grounds upon which proponents argue in favor of amending the anti-cruelty provisions. The study ultimately sheds light on the failure to bring 19th century Canadian criminal laws into the 21st century.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.321
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it