The multiverse of non-financial reporting regulation
Bibliographic record
Abstract
Although non-financial reporting (NFR) has been extensively explored in the accounting literature, most previous studies have focused on relevant issues in contexts where firms report their impact on society and the natural environment voluntarily. Despite the important role previous studies on voluntary NFR play in our understanding over of its role, processes and consequences, extant literature has provided limited evidence on (i) how NFR regulation affects (if at all) corporate reporting, (ii) whether such regulated reporting affects the users of corporate reports and, (iii) whether mandatory NFR has any “real effects” on how firms affect society and natural environment. This special issue attempts to enrich our understanding of the impact of NFR regulation with reference to the studies accepted for the special issue. In addition, this paper discusses key aspects of NFR regulation, provides an overview of the papers included in the special issue and proposes further axes of research in light of the continuing reforms in the NFR regulatory space; which we foresee to lead to a “multiverse” of NFR regulatory models and approaches.
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.
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 | 0.058 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".