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Record W7047920283

Impacts of the TRC and ALRC reports on Indigenous correctional programming and services: a cross-country comparison of Canada and Australia

2021· dissertation· en· W7047920283 on OpenAlexfundaboutno aff

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2021
Typedissertation
Languageen
FieldEngineering
TopicPhotocathodes and Microchannel Plates
Canadian institutionsnot available
FundersGovernment of Western AustraliaGovernment of CanadaU.S. Department of Justice
KeywordsIndigenousAccountabilityCommonwealthGovernment (linguistics)Economic JusticeState (computer science)
DOInot available

Abstract

fetched live from OpenAlex

Indigenous peoples have been overrepresented in the Canadian and Australian criminal justice systems for decades. Commissions were established in both of the commonwealth countries, which produced reports addressing the history of colonization and overrepresentation in the justice system. The reports included calls to action and recommendations to reconcile relationships with the Indigenous populations and improve their wellbeing. This thesis compared the government responses of the Canadian provinces and the Australian state and territory with the highest levels of Indigenous overrepresentation in the correctional system, focusing on correctional programs and services. Publicly available, secondary data was collected and analyzed. Results showed that the level of accountability and action taken in response to the national inquiries varied both within and between countries, from a direct response to an absence thereof. This study can provide insight and direction for best practices in responding to both national inquiries, and the needs of Indigenous prisoners.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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
Published2021
Admission routes2
Has abstractyes

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