ISO Standards: A Platform for Achieving Sustainable Development Goal 2
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".