Effects of stakeholder input on voluntary sustainability standards
Bibliographic record
Abstract
Voluntary sustainability standards can be powerful tools for incentivizing sustainable production practices. Most standards rely on stakeholder input to gain legitimacy and set levels of achievement for businesses at an appropriate level. Yet, the effects of stakeholder input are contentious. Whereas some see stakeholder input leading to more stringent standards, others believe stakeholder input dilutes standards and renders them toothless. I intervene into this debate through an analysis of the effects of stakeholder comments on eight different voluntary sustainability standards. Drawing on an original dataset of 7945 stakeholder comments submitted during public comment periods between 2012 and 2019, I answer three interrelated research questions. First, who comments on sustainability standards and are some groups better represented than others? Second, what types of input do stakeholders provide? Third, which stakeholder comments result in observable changes to the content of sustainability standards? I find that industry groups are over-represented compared to other stakeholder groups. I also find that comments intended to weaken the stringency of sustainability standards are more likely to be implemented than comments intended to strengthen their stringency or other types of comments. A key implication is that stakeholder input is more likely to weaken or maintain the status quo of sustainability standards than strengthen them.
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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 | 0.050 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".