Bibliographic record
Abstract
This book is one of the major outcomes of the Searching for Peace in Asia Pacific project of the European Centre for Conflict Prevention (ECCP).Numerous individuals and organizations from the Asia Pacific region, Europe, the United States, and Canada provided their advice, insights, and support in shaping the project, participated in workshops and seminars, gave feedback on draft texts, contributed chapters for the book, and assisted in the compilation of the directory.The book represents the involvement and ideas of many more persons than those whose names are mentioned as authors.We are indebted to all of them.The project would not have been possible without generous grants from several funding sources.We are grateful to the Ministries of Foreign Affairs of Canada, the Netherlands, and New Zealand, the Department for International Development of the United Kingdom, and the Dutch nongovernmental organization Cordaid for providing financial support.The Searching for Peace in Asia Pacific project started in January 2000 with preliminary research done by the staff of the ECCP.Soon, the need to produce two volumes, in order to give full justice to the region's diversity and to cover this huge continent in sufficient detail, became clear.The first volume, Searching for Peace in Central and South Asia, was published in August 2002.Work on the present volume began in January 2002.The aim of this book is to provide a concise overview of the conflict prevention and peacebuilding efforts in Asia Pacific.Our own staff conducted the initial research, but we relied heavily on the expertise of several practitioners and scholars.We benefited greatly from the intellectual advice and guidance provided by
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.258 | 0.199 |
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".