A comparison of stakeholder engagement practices in voluntary sustainability standards
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Practices of stakeholder engagement vary widely across voluntary sustainability standard setters. This study examines how the sponsorship structure of standard setters affects the diversity of stakeholders included in consultations and the influence of stakeholder input on standards. I compare six sustainability standard setters through an original dataset of 7945 stakeholder comments submitted during public comment periods between 2012 and 2019 to answer two research questions. First, are some standard setters better at balancing stakeholder representation than others? And second, does stakeholder influence vary across standard setters? I find that industry‐sponsored standards tend to attract more industry input than multistakeholder initiatives, but both tend to over‐represent legacy stakeholders. I also find that sponsorship is a poor predictor of which comments will be influential. Comments that seek to weaken or clarify the rules in voluntary sustainability standards are more likely to exert influence irrespective of the sponsorship of the standard setter.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it