Bibliographic record
Abstract
There is a governance gap in the reach of both national and international law, thus leaving companies not legally accountable for potential human rights violations. Closing this governance gap is a critical challenge and one that Penelope Simons and Audrey Macklin strive to address in their book, The Governance Gap: Extractive Industries, Human Rights, and the Home State Advantage. After carefully outlining the limitations of existing laws and initiatives, they argue that home state regulation should play a larger role in deterring corporations from becoming complicit in human rights violations. The potential success of any regulatory proposal to close the governance gap will depend on its ability to effectively shape corporate behaviour. In an effort to achieve this goal, Simons and Macklin recommend a home state governance regime that not only provides ex poste civil liability but is also focused on ex ante prevention of corporate human rights abuses through assessment, monitoring, and disclosure mechanisms. Simons and Macklin’s proposal revolves around mandatory domestic legal mechanisms and a risk-based approach that is centred around due diligence, which would facilitate the operationalizing of human rights within corporate culture. This review essay considers the implications of this proposal in the context of a review of Simons and Macklin’s book.
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 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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".