Professionalizing the assurance of sustainability reports: the auditors’ perspective
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the professionalism and professionalization of sustainability assurance providers based on the experiences and perceptions of auditors involved in this activity. Design/methodology/approach The empirical study was based on 38 semi-directed interviews conducted with assurance providers from accounting and consulting firms. Findings The findings highlight the division of this professional activity between accounting and consulting firms, each of which question the professionalism of the other. The main standards in this area tend to be used as legitimizing tools to enhance the credibility of the assurance process rather than effective guidelines to improve the quality of the verification process. Finally, the complex and multifaceted skills required to conduct sound sustainability assurance and the virtual absence of recognized and substantial training programs in this area undermine the professionalization of assurance providers. Research limitations/implications This work has important practical implications for standardization bodies, assurance providers and stakeholders concerned by the quality and the reliability of sustainability disclosure. Originality/value This study shows how practitioners in this area construct and legitimize their professional activity in terms of identity, standardization and competences. The work contributes to the literatures on the assurance of sustainability reports, self-regulation through standardization and professionalization.
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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 | 0.048 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".