Bibliographic record
Abstract
This book introduces the concept of grey zones of global governance as important and contested spaces for state policy and market behaviour.These grey zones are reflected in the fact that, while operating within the global economy, governments often ignore established rules of international economic law.Governments frequently use subsidies that favour local companies to advance public policy objectives, bend the rules of the multilateral trading system to respond to the perceived dumping of goods, and create innovative policies to respond to structural crises.Since the concept of grey zones of global governance is new, it requires precision in its definition, greater depth in its explanation, and concrete illustrations of its currency.This book aspires to meet these requirements.Although our initial work on this book predates the election of Donald Trump, Brexit, and other backlash against globalization, we believe that these recent political developments reinforce the insights offered in this book.In October 2014 at York University, we workshopped the concept of grey zones of global governance and its public policy implications with a group of researchers from Canada, Europe, Israel, Latin America, and the United States.The majority of the papers were developed into chapters for this volume.We are deeply appreciative of the insights and contributions of the participants in shaping our own thoughts on grey zones of global goverance.This volume has been made possible by support from the Major Collaborative Research Initiatives (MCRI) program of the Social Sciences and
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.390 | 0.280 |
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; the direct Gemma label and the distilled Codex classifier 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".