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Record W2328554993

The Civil Justice System and the Public Justice for Nunavummiut: Partnerships for solutions

2008· article· en· W2328554993 on OpenAlexaffabout
Travis Anderson, Mary Stratton

Bibliographic record

VenueeYLS (Yale Law School) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEconomic JusticePolitical scienceCriminologySociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

As part of the Civil Justice System and the Public (CJSP), a national collaborative research project, we first visited Iqaluit in June 2003. At that time the Research Coordinator met with key contacts in the Nunavut justice and social service community to talk about the research and make plans for conducting the field research. As a result of these initial meetings, the CJSP team made contact with Inuit services in Ottawa. In July 2003, during the Ontario phase of the CJSP research, we met Inuit service providers and several Nunavummiut who were at that time living in Ottawa.1 In September 2003, the CJSP Research Team came to Iqaluit and over a period of two weeks completed 28 in-depth interviews. Eighteen people (eleven women and seven men) worked within the justice community and included members of the judiciary as well as court administration and frontline staff. We also interviewed ten members of the public (two women and eight men) who were either personally involved in court cases at varying stages of resolution, or acting as community advocates to people with legal problems. In order to increase the team’s understanding of Nunavut, researchers also compiled observation notes and held many informal conversations with Iqaluit residents as well as key contacts in some other Nunavut communities.

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)0.010
metaresearch head score (Gemma)0.010
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: none
Teacher disagreement score0.955
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0450.026
Scholarly communication0.0230.017
Open science0.0040.031
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0150.001

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 head0.089
GPT teacher head0.310
Teacher spread0.221 · 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
Published2008
Admission routes2
Has abstractyes

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