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Record W4366257195 · doi:10.1002/bse.3424

The uneasy marriage of private standards and public policies for sustainable commodity governance

2023· article· en· W4366257195 on OpenAlexafffund
Hamish van der Ven, David E. Barmes

Bibliographic record

VenueBusiness Strategy and the Environment · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsUniversity of British Columbia
FundersFonds de Recherche du Québec-Société et Culture
KeywordsCorporate governanceCommodity chainCommoditySustainabilityComplementarity (molecular biology)Public goodBusinessFair tradeCertificationGreenwashingTransparency (behavior)EconomicsMarket economyInternational tradeProduction (economics)FinancePolitical science

Abstract

fetched live from OpenAlex

Abstract Global value chains for commodity agriculture have been the target of a broad array of sustainability governance efforts led by both public and private actors. In this paper, we ask: how do public policies and private standards interact in commodity‐exporting countries? To answer this question, we examine the interactions between voluntary sustainability standards and domestic public policies in three cases: soybean farming in Brazil, palm oil production in Indonesia, and pangasius aquaculture in Vietnam. We find that in each case, public and private governance interactions go through a period of competition before ending in a state of reluctant complementarity. We argue that this reluctant complementarity results from the need of governments in commodity‐producing countries to maintain export markets for their goods. This finding challenges the idea that complementarity between public and private sustainable commodity governance is driven by goal alignment and tempers expectations that standards and certifications can mitigate deforestation.

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)0.018
metaresearch head score (Gemma)0.023
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: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.024
Scholarly communication0.0130.009
Open science0.0010.009
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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

Citations16
Published2023
Admission routes2
Has abstractyes

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