Bibliographic record
Abstract
This pioneering Research Handbook provides a comprehensive and in-depth scholarly overview of the field of soft law, exploring the scope of current thinking in the field as well as proposing future pathways for soft law research. Organized into four broad themes, the Research Handbook offers important and unique insights into the dynamic and complex nature of soft law. The first section delves into the conceptual history and development of soft law. Second, the Handbook explores the disciplinary understandings of soft law, examining how scholars from different fields investigate the topic. The third theme focuses on the public and private actors and institutions involved in soft law-making, providing a detailed analysis of the complex relationships that shape soft law. Finally, the fourth theme explores the role of soft law in addressing major global societal challenges, including among others climate change, gender inequality, and the regulation of artificial intelligence. This Research Handbook will be a key resource for students and scholars in constitutional and administrative law, public international law, regulation and governance, public administration and policy, and law and politics. Practitioners and policymakers seeking to better understand the role of soft law in domestic and international law, policy and governance will also find this book beneficial.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.035 |
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".