Bibliographic record
Abstract
Experts explore current theory and practice in the application of digitally enabled open networked social models to international development. The emergence of open networked models made possible by digital technology has the potential to transform international development. Open network structures allow people to come together to share information, organize, and collaborate. Open development harnesses this power, to create new organizational forms and improve people's lives; it is not only an agenda for research and practice but also a statement about how to approach international development. In this volume, experts explore a variety of applications of openness, addressing challenges as well as opportunities. Open development requires new theoretical tools that focus on real world problems, consider a variety of solutions, and recognize the complexity of local contexts. After exploring the new theoretical terrain, the book describes a range of cases in which open models address such specific development issues as biotechnology research, improving education, and access to scholarly publications. Contributors then examine tensions between open models and existing structures, including struggles over privacy, intellectual property, and implementation. Finally, contributors offer broader conceptual perspectives, considering processes of social construction, knowledge management, and the role of individual intent in the development and outcomes of social models. Contributors Carla Bonina, Ineke Buskens, Leslie Chan, Abdallah Daar, Jeremy de Beer, Mark Graham, Eve Gray, Anita Gurumurthy, Havard Haarstad, Blane Harvey, Myra Khan, Melissa Loudon, Aaron K. Martin, Hassan Masum, Chidi Oguamanam, Katherine M. A. Reilly, Ulrike Rivett, Karl Schroeder, Parminder Jeet Singh, Matthew L. Smith, Marshall S. Smith Copublished with the International Development Research Centre of Canada (IDRC)
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.299 | 0.190 |
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".