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Record W6958168697 · doi:10.60770/p4tf-d018

Tangible change or deaf ears?

2024· article· en· W6958168697 on OpenAlexaffabout

Bibliographic record

VenueMRU-Repo · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsMount Royal University
Fundersnot available
KeywordsIndigenousEconomic JusticeAction (physics)Law enforcementCurriculumEnforcement

Abstract

fetched live from OpenAlex

Since the release of the Truth and Reconciliation Commission’s (TRC) Calls to Action in 2015, there has not been any research pertaining to their uptake in law schools and justice programs across Canada. This study involved a literature review and an environmental scan of 17 law schools and 25 justice-related programs across Canada and examined their respective curriculums to determine whether and how they have shown consideration for the TRC’s 28th and 63rd Calls to Action. It was posited that, if lawyers and law enforcement workers are not being educated on Indigenous issues while they are in school then they will be more prone to negative interactions with Indigenous clients and individuals. Ultimately, this could contribute to the significant overrepresentation of Indigenous persons in custody in the Canadian justice system. The findings indicated that there is indeed a lack of consideration for the Calls to Action - more so for justice programs than law schools but apparent in both nonetheless. Further research will be required to explore this issue in depth, including what barriers prevent programs and schools from instituting the Calls to Action, and to ensure that academic institutions are contributing to reconciliation efforts and making tangible steps towards change.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.806
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.277
GPT teacher head0.508
Teacher spread0.231 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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