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Record W3102011194 · doi:10.1017/lsi.2020.18

Building the Aboriginal Conference Settlement Suite: Hope and Realism in Law as a Tool for Social Change

2020· article· en· W3102011194 on OpenAlexaffabout
Toby S. Goldbach

Bibliographic record

VenueLaw & Social Inquiry · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLawIndigenousSettlement (finance)Economic JusticeMainstreamSociologyCriminal lawGovernment (linguistics)Legal historyPolitical science

Abstract

fetched live from OpenAlex

In 2014, the provincial government unveiled a new courthouse in Thunder Bay, Ontario, featuring a conference area designed to emulate an Anishinaabe roundhouse. The “Aboriginal Conference Settlement Suite” epitomizes efforts to support Indigenous justice within the criminal justice system. However, despite similar efforts in the past, the circumstances of Indigenous peoples in Canada have not improved. This ongoing commitment to legal solutions is emblematic of mainstream views of law as a problem-solving instrument. Notwithstanding awareness of its failings, law reformers remain dedicated to using law as a tool for social change. Employing a case study method focusing on the new courthouse, I challenge a prevailing wisdom that law reform outputs are manageable and in our control. I argue that similar to a courthouse, which is a concrete, physical structure as well as a symbol of justice, so too is the legal instrument both material and metaphorical, with concrete outcomes and symbolic forms. While treating law as a literal tool may give law reformers a longed-for sense of mastery, this approach belies law’s diffuse constitutive power and the various paradoxes in reformers’ actions. Accepting law’s dual nature is essential for candid and accurate assessments about the possibilities and limits of change through law.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
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 head0.122
GPT teacher head0.401
Teacher spread0.279 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2020
Admission routes2
Has abstractyes

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