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Record W4387770356 · doi:10.46697/001c.88529

Impact Investing and Sustainable Global Value Chains: Enabling Small and Medium Enterprises Sustainability Strategies

2023· article· en· W4387770356 on OpenAlex
Tommaso Ferretti

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIB Insights · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSustainabilityBusinessNexus (standard)Multinational corporationProduction (economics)PopulationValue (mathematics)Emerging marketsNatural resource economicsIndustrial organizationFinanceEconomics

Abstract

fetched live from OpenAlex

Improving the sustainability of SMEs in developing and emerging economies, which represent the vast majority of the population of MNCs’ supplier networks, is fundamental to achieving the Sustainable Development Goals. However, SMEs often lack viable financing options to invest in their sustainability. Emergent impact investing seeking social, environmental, and financial returns aims to address this financing gap. How does impact investing influence sustainability in the global value chains of MNCs? Studying the nexus between impact investing and the strategies of SMEs in Latin America’s coffee and forestry sectors, I provide new insights into how the modes of financing suppliers’ production activities improve GVC sustainability.

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.

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)0.001
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 score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.278
Teacher spread0.237 · 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