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Record W4323320071 · doi:10.4324/9781003182375-5

Digging for accountability in Canada

2023· book-chapter· en· W4323320071 on OpenAlexaboutno aff
Angela M. Asuncion, Nicolas D. Brunet, Dominique Caouette

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsDiggingAccountabilityPolitical scienceGeographyArchaeologyLaw

Abstract

fetched live from OpenAlex

Canada is a prominent leader in the global extractive sector, with more than 800 Canadian mining corporations active in over 100 countries across the globe. Canadian mining assets overseas are valued at $144.2 billion, accounting for approximately 65% of the nation’s total mining assets. However, Canada’s dominance in the international mining industry has come at a cost, especially for the Global South. Historically, Canadian mining corporations have been under scrutiny for taking advantage of weak legal systems in underdeveloped nations. The public has become increasingly aware of alleged human rights abuses and socio-environmental disasters involving Canadian mining operations overseas. Despite these behaviours, there remains an absence in global regulatory treaties litigating corporate accountability in the extractive industry. Liabilities from mining externalities have consequently been ignored through non-binding international frameworks, national policies, and CSR. However, the legitimacy of global frameworks and CSR practice have been called into question as socio-environmental negligence remains unabated across the Global South’s extractive sector. This chapter reviews the international legal systems, national policies, and CSR mechanisms regulating the Canadian mining industry in the Global South. It specifically addresses gaps in knowledge related to Canadian foreign ownership and CSR practice in underdeveloped nations, exploring the impact of Toronto Ventures Incorporated within the Philippines as a case study for analysis.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.130
Threshold uncertainty score0.942

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0210.011
Scholarly communication0.0140.004
Open science0.0010.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0160.002

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 head0.045
GPT teacher head0.209
Teacher spread0.164 · 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
Published2023
Admission routes1
Has abstractyes

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