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Record W2621013642 · doi:10.1016/j.exis.2017.05.011

The evolving role of CSR in international development: Evidence from Canadian extractive companies’ involvement in community health initiatives in low-income countries

2017· article· en· W2621013642 on OpenAlexaffabout
Sarah E Lamb, Jonathan Jennings, Philippe Calain

Bibliographic record

VenueThe Extractive Industries and Society · 2017
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsEngineers Without Borders Canada
Fundersnot available
KeywordsCorporate social responsibilityBusinessEconomic growthPublic relationsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Overseas development agencies and international finance organisations view the exploitation of minerals as a strategy for alleviating poverty in low-income countries. However, for local communities that are directly affected by extractive industry projects, economic and social benefits often fail to materialise. By engaging in Corporate Social Responsibility (CSR), transnational companies operating in the extractive industries ‘space’ verbally commit to preventing environmental impacts and providing health services in low-income countries. However, the actual impacts of CSR initiatives can be difficult to assess. We help to bridge this gap by analysing the reach of health-related CSR activities financed by Canadian mining companies in the low-income countries where they operate. We found that in 2015, only 27 of 102 Canadian companies disclosed information on their websites concerning health-related CSR activities for impacted communities. Furthermore, for these 27 companies, there is very little evidence that alleged CSR activities may substantially contribute to the provision of comprehensive health services or more broadly to the sustainable development of the health sector.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.270
Teacher spread0.242 · 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 designObservational
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

Citations25
Published2017
Admission routes2
Has abstractyes

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