Sustainability Certifications as Tools of Normalizing Governance
Bibliographic record
Abstract
Sustainability certifications are an increasingly common tool used by multinational enterprises (MNEs) and others to cascade norms about sustainability across global value chains (GVCs). They are also highly contested, leading to accusations of neo-colonialism and greenwashing. I consider how MNEs might more effectively cascade norms about sustainability across their GVCs by ensuring certifications integrate the local concerns of their GVC actors with the global concerns of the MNE. I zero in on social sustainability risk—defined as negative impacts on human rights and basic needs—and explore how the indicators in certification texts can be selected to better integrate local and global perspectives. Based on both 28 semi-structured interviews and in-depth analysis of two certification standards, I develop a model guided by convention theory (CT) that reveals how certification standards can help actors reach a compromise on different risk perceptions through language that generates composite arrangements. My model contributes to the debates about how MNEs can cascade norms across GVCs by focusing attention on the importance of written documents like certification standards that serve as potential sites of compromise and alignment between global and local risk concerns.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".