Technology-enhanced Auditing in Voluntary Sustainability Standards: The Impact of COVID-19
Bibliographic record
Abstract
The ongoing COVID-19 pandemic has had a significant impact on the certification and auditing services of Voluntary Sustainability Standards (VSS). The traditional approach to auditing—on-site visits—has been significantly curtailed, and it is unclear when, and under what conditions, it might resume in full. The purpose of this paper is to study the initial responses to COVID-19 of leading VSS—a group of 21 standards that are members of ISEAL, a global membership organization for VSS. This is a qualitative study, and data are collected from publicly-available sources (i.e., official announcements, policy amendments, derogations) in order to inductively analyze how individual VSS have adjusted their certification services in response to travel bans and lockdowns. The emphasis of the analysis was understanding the role of technologies in the VSS responses to the COVID-19 crisis. The findings demonstrate significant uptake of remote auditing and information and communications technology (ICT), even though that uptake is constrained by limiting conditions and it is not currently expected by VSS to extend beyond the crisis. Lessons learned from the crisis are discussed, and the potential for remote auditing during this period to encourage the adoption of more advanced technologies (such as artificial intelligence and satellite monitoring) in certification services is explored. A set of research questions to guide future work grounded in the analysis is also provided.
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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 | 0.029 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".