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Record W4212786445 · doi:10.1080/09640568.2022.2030684

Multinational financial corporations and the sustainable development goals in developing countries

2022· article· en· W4212786445 on OpenAlexafffund
Eduardo Ordonez‐Ponce, Olaf Weber

Bibliographic record

VenueJournal of Environmental Planning and Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of WaterlooAthabasca University
FundersAthabasca University
KeywordsMultinational corporationDeveloping countrySustainabilityBusinessSustainable developmentEmerging marketsDeveloped countryFinanceAccountingEconomic growthEconomicsPolitical science

Abstract

fetched live from OpenAlex

Multinational financial corporations are key to sustainability by implementing practices disclosed through sustainability reports. This is where research has been focused, leaving a gap concerning their sustainability foci on developing countries. This article studies the largest financial corporations from developed and emerging countries identifying the SDGs on which they focus in developing countries, the evolution of their contributions and differences in the SDGs, and where their focus is in the developing world. The largest multinational financial corporations were selected, their sustainability reports assessed, and mixed methods conducted finding that the foci of those from developed countries vary across SDGs, countries of origin, impacted developing countries, and since the launch of the SDGs. Findings highlight the SDGs on which financial corporations focus, with those from developed countries implementing more practices than those from emerging economies, and that the contributions of multinational financial corporations have not affected the progress of the SDGs.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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 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

Citations18
Published2022
Admission routes2
Has abstractyes

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