Bibliographic record
Abstract
This chapter examines a community of professionals, created by a government agency and charged with conducting country-wide, cross-disciplinary, and cross-sectoral research and innovation in the area of water. The analysis describes the structure of the community and places it in the context of existing project practices and institutional arrangements. Under challenging conditions, the professionals in the area recruit team members from their trusted long-term collaborators, work independently on projects, use standard communication technologies and prefer informal face-to-face contacts. Out of these practices emerge a sparsely connected community with permeable boundaries interspersed with foci of intense collaboration and exchange of ideas. In this community, professionals collaborate and exchange of ideas with the same colleagues. Both collaboration and exchanges of ideas tend to involve professionals from different disciplines and, to a lesser extent, from different sectors and locations.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".