Bibliographic record
Abstract
Recent domestic legislation is blurring the line between securities regulation and human rights law. Securities law has traditionally regulated corporate disclosure on financial information, such as income statements and investment risks. By contrast, human rights law has traditionally operated in the international sphere and focused on state obligations. That all changed in 2010 with the adoption of the Dodd-Frank Wall Street Reform and Consumer Protection Act, which includes sections 1502 and 1504 on non-financial disclosure related to human rights and anti-corruption. In particular, section is the first regulation to create binding rules on due diligence with regard to a company’s supply chain. It imposes a new reporting requirement on publicly traded companies that manufacture products using certain conflict minerals. Companies must identify whether the sourcing of the minerals originated in the Democratic Republic of Congo (DRC) and bordering countries. If so, they must submit an independent private sector audit report on due diligence measures taken to determine whether those conflict minerals directly or indirectly financed or benefited armed groups in the covered countries. The Dodd-Frank provisions are but one example of an emerging trend in international securities law. Over the past decade, an increasing number of governments and securities exchanges have passed mandatory regulations on corporate disclosure of social issues. In this Article, I take a step back from these recent developments to analyze a critical question: Is securities regulation the appropriate mechanism for achieving human rights compliance? By doing so, I seek to open a dialogue between two disparate streams of scholarship in private and public law and propose policy recommendations for effectively furthering the movement towards corporate accountability. While existing literature on sections 1502 and 1504 addresses the history of the legislation and critiques its efficacy, the main contribution of the Article is to analyze the normative implications of the broader strategy of using securities regulation to hold companies accountable for human rights abuses.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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; both teacher heads agree on what is shown here.
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".