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Record W4365145211 · doi:10.1017/cls.2023.9

Extracting Profits: State Regulation and the Canadian Ombudsperson for Responsible Enterprise

2023· article· en· W4365145211 on OpenAlexaffabout
Kristine Johnston, Steven Bittle

Bibliographic record

VenueCanadian Journal of Law and Society / Revue Canadienne Droit et Société · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBlameDominance (genetics)IdeologyImpunityHarmGovernment (linguistics)Political scienceState (computer science)Political economyLaw and economicsSociologyLawPoliticsSocial psychology

Abstract

fetched live from OpenAlex

Abstract Taking the Canadian Ombudsperson for Responsible Enterprise (CORE) as its focus, this paper critically examines the Canadian government’s efforts to regulate the extractive industry. Using insight from ideology theory and critical discourse analysis, and drawing empirically from Canadian Parliamentary debates, official government and NGO reports, and various news items regarding the development of the CORE, we document how dominant voices prioritized the economy, downplayed the systemic violence of the industry, and redirected blame to “underdeveloped” countries, on route to a regulatory framework that is voluntary and which fails to address the underlying causes of corporate harm and violence. While the CORE represents a “logical” state response to corporate crime, we nevertheless emphasize the importance of ongoing debates about its role in combating corporate impunity. This not only reinforces the idea that (capitalist) dominance is never absolute but signals the ever-present nature of resistance and possibility for change.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.255
Teacher spread0.230 · 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 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
Published2023
Admission routes2
Has abstractyes

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