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Record W2945867602

Effectiveness Framework for Home-State Non-Judicial Grievance Mechanisms

2019· article· en· W2945867602 on OpenAlexaffabout
Charis Kamphuis, Leah Gardner

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicOmbudsman and Human Rights
Canadian institutionsMcGill UniversityThompson Rivers University
Fundersnot available
KeywordsGrievanceLegitimacyHuman rightsPolitical scienceContext (archaeology)Law and economicsJudicial reviewGovernment (linguistics)State (computer science)Public administrationLawBusinessSociologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

After more than fifteen years of public debate, law reform proposals and pressure from international human rights bodies, the Canadian government announced in early 2018 that it intends to create the Canadian Ombudsperson for Responsible Enterprise (CORE). While the CORE mechanism is still in the developmental phase, it is expected that it will have the power to independently investigate allegations of human rights violations against Canadian companies operating abroad in the natural resources and garment sectors. As such, it will be the first home-state non-judicial grievance mechanism of its kind in the world. However, the concept of such a mechanism is not new, but has in fact been the subject of discussion among international human rights institutions for at least ten years, including with significant commentary on the Canadian context. This chapter complies and analyzes existing statements of public international law with respect to these kinds of mechanisms. It then identifies points of consensus and argues that there is an emerging consensus with respect to the principles that such mechanisms should abide by in order to ensure their effectiveness and legitimacy. This analysis will be of significant interest to those in Canada who are working to ensure that the CORE will provide an effective remedy for affected individuals and groups, and to others around the world who hope to create similar mechanisms.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.283
Teacher spread0.276 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2019
Admission routes2
Has abstractyes

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