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Record W3200729752 · doi:10.3138/jcs-2020-0051

Undermining Justice: The Political Framing of Actors in the Independent Assessment Process

2021· article· en· W3200729752 on OpenAlexvenueaboutno aff
Sidey Deska-Gauthier, Leah Levac, Cindy L. Hanson

Bibliographic record

VenueJournal of Canadian Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsFraming (construction)AdjudicationSociologyPoliticsColonialismLawPublic administrationPolitical scienceCriminology

Abstract

fetched live from OpenAlex

This article presents findings from a critical discourse analysis of House of Commons debates about the Independent Assessment Process (IAP), an out-of-court compensatory adjudication process intended to resolve claims of sexual and physical abuse that occurred at Indian Residential Schools and one of five key elements of the Indian Residential School Settlement Agreement. Our analysis is guided by the question: What do elected officials’ discussions about the IAP reveal about the implementation of compensatory transitional justice mechanisms in settler colonial states, and about colonial relations (specifically attempts at reconciliation) more generally? Our study focuses on debates that took place between 2004 and 2019. We explored elected officials’ framing of both Survivors and the Canadian State in their discussions about the IAP. Our analysis reveals the limited reach of dialogue based in a partisan and antagonistic context and supports those scholars who assert that transitional justice is incompatible with reconciliation and decolonization. By way of contributing to the larger interdisciplinary study entitled Reconciling Perspectives and Building Public Memory: Learning from the Independent Assessment Process, of which this article is part, we reflect on what our findings mean not only for public memory but also for studying the IAP moving forward.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.065
GPT teacher head0.426
Teacher spread0.361 · 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 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

Citations2
Published2021
Admission routes2
Has abstractyes

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