Bibliographic record
Abstract
In a deeply iniquitous world, where the gains from trade are distributed unevenly and where trade rules often militate against progressive social values, human health, and sustainable development, NGOs are widely touted as our best hope for redressing these conditions. As a critical voice of the poor and marginalized, many are engaged in a global struggle for democratic norms and social justice. Yet the potential for NGOs to bring about meaningful change is limited. This book examines whether improvements in participatory opportunities for progressive NGOs results in substantive and normative policy change in one of the major trading powers, the European Union. Hannah advances a constructivist account of the role of NGOs in the EU’s trade policymaking process. She argues that NGOs have been instrumental in providing education, raising awareness, and giving a voice to broader societal concerns about proposed trade deals, both when they take advantage of formal participatory opportunities and when they protest from the streets and in the media. However, the book also highlights how NGO inputs are mediated by the social structure of global trade governance. Epistemes—the background knowledge, ideological and normative beliefs, and shared assumptions about how the world works—determine who has a voice in global trade governance. Showing how NGOs succeed only when their advocacy conforms broadly to the dominant episteme, this book will be of value to scholars and students with an interest in NGOs and international trade negotiations. It will also be of interest to policymakers, national trade negotiators, government departments, and the trade policy community.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 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".