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Record W2908439802

Climate Change, Corporate Social Responsibility, and the Extractive Industries

2017· article· en· W2908439802 on OpenAlexaffabout
Sara L. Seck

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCorporate social responsibilityHuman rightsClimate justiceClimate changeClimate change mitigationCorporate governancePolitical scienceDutyBusinessPolitical economy of climate changePreambleNegotiationLaw and economicsLawEconomicsFinanceEngineering
DOInot available

Abstract

fetched live from OpenAlex

During the negotiation of the Paris Agreement, many argued that the final text should integrate a human rights approach so as to better align climate governance under the UNFCCC with climate justice. Reference to human rights ultimately appeared only in the Preamble, despite submissions from the UN High Commissioner for Human Rights that urgent and ambitious State action to combat climate change is an existing duty of international human rights law. Another submission highlighted the role of businesses as duty-bearers who must contribute to climate mitigation and be accountable for climate impacts.\nThis article will consider an unexplored avenue through which Canada could advocate for climate mitigation by examining whether Canada’s corporate social responsibility (CSR) strategy for Canadian extractive sector companies operating internationally integrates consideration of human rights and climate change. The paper will argue that while Canada’s CSR strategy expects Canadian extractive companies, including oil & gas companies, to respect human rights wherever they operate, the international CSR standards endorsed in the Strategy are largely climate mitigation bind. Moreover, many of these standards are also gender blind, of importance given that women of the global south are among the most vulnerable to the human rights impacts of climate 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
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 score0.043
Threshold uncertainty score1.000

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 studies0.0040.001
Scholarly communication0.0020.002
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 head0.057
GPT teacher head0.263
Teacher spread0.206 · 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

Citations3
Published2017
Admission routes2
Has abstractyes

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