Bibliographic record
Abstract
The aim of this paper is to analyze the Local Authorities management reports, particularly those of nine companies involved in the water distribution services in light of the changes made by the legislator to Article 2428 c.c. The new article of the Civil Code has, indeed, provided for the integration, in the management report, of a series of figures and prospective non-financial tools, regarding the environment, employees relationships and the company’s performance, and provided information on developments of management, strategies, plans and business programs. The operational methodology used has been based on a detailed analysis of the existing literature on the matter and on an empirical analysis that has been actualized in the investigation of document sources (management reports and, where possible, social balance reports). What has emerged from the analysis is that, except that for few exceptions, there is a general lack of information on environment, risks and personnel. Indeed, through the recent changes and additions, the legislator has, in effect, made mandatory, albeit in an indirect (i.e. through the editing of the management report), the drafting of a mini social statement. This was necessary in light of the fact that the recent evolution of the role played by companies, and local authorities in particular, has led to the recognition of a social and environmental dimension of their activities, which combines and integrates with the management, economic, financial and competitive profiles.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.159 | 0.093 |
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".