The Relationality of Community Development Agreements towards a Human Rights Due Diligence Good Faith Requirement
Bibliographic record
Abstract
Abstract Human rights due diligence (HRDD) is a buzzword in business and human rights (BHR) activities. However, multinational corporations (MNCs) often conduct it as a tick-box exercise without transparency. Using a relational contract theory, this article argues that when MNCs contract with local communities through community development agreements (CDAs) to perform HRDD, such contracts are internationalized relational contracts that attract a level of good faith. An established principle in international economic law, good faith serves as a standard for assessing conduct designed to discharge obligations in international contracts between states and MNCs (investor-state contracts). Similar to how investor-state arbitration tribunals use good faith jurisprudence in regulating the relationship between states and MNCs, this article proposes a BHR good faith jurisprudence to prescribe how HRDD obligations should be discharged. The article concludes that a good faith interpretational exercise in BHR would (1) reduce MNCs’ cosmetic compliance with HRDD principles; (2) increase transparency in the HRDD exercise; and (3) become a source of rights for local communities to enforce corporate accountability.
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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 | 0.030 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".