Researching the culture in agri-culture: social research for international development
Bibliographic record
Abstract
Dedication: Tribute to Robert King Merton, as a Founder of the Sociology of Science Foreword, Emil Q Javier and Per Pinstrup-Andersen Acknowledgements Contributors List of Abbreviations PART 1: SOCIAL RESEARCH FOR AGRICULTURAL POLICIES Concept and Method: The Uphill Route for Social Research in a Technological Environment, M M Cernea Agricultural Institutions and Receptivity to Social Research: The Case of the CGIAR, A H Kassam Who Are the Social Researchers of the CGIAR System? E Rathgeber, University of Ottawa, Canada PART 2: THE INSIDERS' VIEWS: SOCIAL RESEARCH IN THE CGIAR SYSTEM Rice for the Poor: The Call and Opportunity for Social Research, T R Paris, DAPO, Phillippines, S Morin, F G Palis, and M Hossain Understanding Forests-People Links: The Voice of Social Scientists, C J Pierce Colfer, CIFOR, Indonesia with E Dounias, M Goloubinoff, C Lopez, and W Sunderlin Humanizing Technology Development in the Green Revolution's Home, M R Bellon, CIMMYT, Mexico, M Morris, J Ekboir, E Meng, H De Groote, and G Sain Water to Thirsty Fields: How Social Research Can Contribute, M Samad, International Water Management Institute, Sri Lanka and D J Merrey Rootcrops in Agricultural Societies: What Social Research has Revealed, G Prain, CGIAR System-wide initiative on Urban & Peri-urban Agriculture, Lima, Peru, G Thiele, O Ortiz, and D Campilan Why the 'Livestock Revolution' Requires Research on People, D Romney, ILRI, Nairobi, Kenya and B Minjauw Aquatic Resources: Collective Resources and Severance for the World's Fish Wealth, K Kuperan Viswanathan, World Fish Center, Dhaka, Bangladesh, M Ahmed, P Thompson, P Sultana, M Dey, and M Torell Tropical Agriculture and Social Research: An Analytical Perspective, D Holland, Greening Australia, Inc, Australia, J Ashby, M Mejia, and J Voss. Dry Areas and the Changing Demands for Social Research, A A Aw-Hassan, International Center for Agricultural Research in the Dry Areas (ICARDA), Syria and M Abdelali-Martini Agricultural Biodiversity-and How Human Culture is Shaping It, P Eyzaguirre, IPGRI, Italy Studying Property Rights and Collective Action: A System-Wide Program, R Meinzen-Dick, CGIAR, USA Crafting Food Policy with Social Science Knowledge, R Meinzen-Dick, M Adato, M Cohen, C Farrar, L Haddad, and A Quisumbing PART 3: THE OUTSIDERS' VIEW: ISSUES, EXPECTATIONS, AND AGENDAS Not Just One Best System: The Diversity of Institutions for Coping with the Commons, E Ostrom, Indiana University, USA Social Research and Researchers in the CGIAR: Perceiving an Underused Potential, R Chambers, University of Sussex, UK The Rockefeller Foundation and Social Research in Agriculture, G Conway, The Rockefeller Foundation, USA, A Adesina, J Lynam, and J Moock A Donor Perspective on the Accomplishments, Limitations, and Opportunities for Social Research, S Bode, USAID/EGAT/ESP/IRB, USA and D Rubin Seeking Half our Brains: Constraints and Incentives in the Social Context of Interdisciplinary Research, R E Rhoades, University of Georgia, USA Roots: Reflections of a 'Rocky Doc' on Social Science in CGIAR, S Guggenheim, The World Bank, USA Social Science Knowledge as Public Good for Agriculture, D G Dalrymple, US Agency for International Development, USA Index.
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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 | 0.043 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 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".