Reviewing the Non-Financial Reporting Directive : An analysis de lege lata and de lege ferenda concerning sustainability reporting obligations for undertakings in the EU
Bibliographic record
Abstract
The Non-Financial Reporting Directive (“NFRD”),[1]is an important contributor to the European Union’s (EU) goal of creating a more sustainable future for all. By requiring large public-interest entities to report non-financial information relating to sustainability matters, the NFRD increases business transparency and gives stakeholders the opportunity to make more informed investment decisions, monitor corporate activities and initiate discussions based on current practices. The purpose of this thesis is to analyze the NFRD as it stands today and to analyze in what way the NFRD has the potential to improve by chiefly using the legal dogmatic method. The thesis reached its completion with an appropriate timing (January 2021) as the EU has announced its ambition to revise the NFRD by the first quarter of 2021. The conclusion drawn is that the NFRD should be revised on a series of points. Most importantly, reliability of the provided information should be secured through a stronger verification mechanism. Other areas for improvement concern the enlargement of the scope of the NFRD and the implementation of further measures securing comparable data. [1]Directive 2014/95/EU.
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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 | 0.092 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".