Investigating the Quality of Gender Equality Non-Financial Information Disclosed in the Cooperative Credit Sector: A Case Study
Bibliographic record
Abstract
Credit institutions, according to the 2014/95/EU Directive (implemented in Italy with Legislative Decree No. 254/2016) are obliged to report non-financial and diversity information. Our article focuses on the diversity information to investigate whether the obligation to disclose diversity information within the mandatory non-financial statement (NFS) led to an improvement of the quality of the gender equality information. To address this aim we analyzed five consolidated mandatory NFSs (CNFSs) for the Iccrea Cooperative Banking Group (ICBG) covering the 2017–2021 period. We selected ICBG because of the dearth of studies on the cooperative banking sector, which represent a relevant component of the national banking system in Italy. To the best of our knowledge, this paper is the first study to explore the quality of information on gender equality in mandatory NFSs for a cooperative banking group using a longitudinal approach. The analysis of the case study’s findings provides evidence that ICBG worked to align its gender information with the Decree requirements and the GRI standards. The longitudinal analysis highlights that, during the five years under study, the ICBG’s information on gender came to fully reflect the EU and Italian requirements.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".