Bibliographic record
Abstract
This book - one in the four-volume set, Global Governance and the Quest for Justice - focuses on the role of corporations in an increasingly globalised world. Against the backcloth of perceived abuse of corporate power - alleged violations of human rights, degradation of the environment, abuse of labour, Enron-style financial scandals, and the like - the chapters in this collection examine the nature and function of the corporation as well as the way in which we should understand corporate governance and the power of transnational corporations. Central to the question is the issue of accountability, as well as the questions of social and environmental responsibility - here the authors ask whether corporations should be more accountable relative to the broader public interest, and suggest that public law approaches to accountability may offer a way forward. Consideration is also given to the most appropriate regulatory locus (local, regional, or international) and the most effective form of response to the deficit in corporate responsibility and the abuse of corporate power. For example, are transnational corporations most effectively regulated internationally (e.g., by the United Nations), regionally (e.g., by the EU or NAFTA) or locally (e.g., through stringent reporting requirements and implementation of triple bottom line standards)?
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".