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Record W3098143153 · doi:10.3390/su12229332

ISO Standards: A Platform for Achieving Sustainable Development Goal 2

2020· article· en· W3098143153 on OpenAlexaff
Xiaoli Zhao, Pavel Castka, Cory Searcy

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStandardizationCertificationBusinessSustainable developmentProcess managementFood securityScale (ratio)AgricultureCorporate governanceStakeholderKey (lock)Environmental resource managementComputer sciencePolitical scienceEconomicsManagementPublic relationsFinanceGeographyComputer security

Abstract

fetched live from OpenAlex

The International Organization for Standardization (ISO) has recently begun promoting the linkages between its standards and the United Nations’ Sustainable Development Goals (SDGs). However, further research is needed to explore how ISO standards can serve as a platform for achieving the SDGs. In this paper, we discuss the interlinkage between ISO standards and SDG 2 (i.e., Zero hunger—End hunger, achieve food security and improved nutrition, and promote sustainable agriculture). We review the literature on a set of 77 ISO standards related to SDG 2 and study existing evidence pertinent to SDG 2 and its targets. Specifically, we review research in four key areas of interest: agricultural productivity of ISO certified firms, adoption of ISO standards amongst small scale producers, ISO standards development, and governance of standards. We found implicit evidence in the literature that ISO standards have the potential to contribute to two SDG 2 targets, namely targets 2.3 and 2.4. Some aspects of ISO standards, however, such as low levels of adoption amongst small scale farmers or a lack of multi-stakeholder standard development, contradict key aspects of the SDG agenda. We outline key areas for future research in the four areas of interest noted above.

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)0.002
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.021
GPT teacher head0.276
Teacher spread0.256 · 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 designNot applicable
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

Citations37
Published2020
Admission routes1
Has abstractyes

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