Look Upstream: Measurement for Innovation on the Upper Rio Negro of the Amazon Basin
Bibliographic record
Abstract
The growth of the randomized controlled trial (RCT) as the "gold standard" for evaluation has justly been praised as an advance in the professionalization of social programs and projects, an "adoption of science" - in the words of the Lancet. None the less, the emphasis on the RCT biases funding for projects that distribute private goods and which focus on "low hanging fruit" in health, nutrition, and sanitation, simply because those areas lend themselves to the sort of measurement that works with RCTs. As a result, many project developers in the government and NGO sectors lament that a hegemonic focus on RCTs impedes creativity or new models that challenge traditional paradigms. This case study of CanalCanoa, a community video coaching project for indigenous parents of young children in the Rio Negro region of the Amazon Basin, offers techniques to measure for innovation. Instead of developing a new RCT for an extremely diverse population (27 ethnic groups) where traditional childcare methods are in historical flux because of urbanization, CanalCanoa measured variables shown by previous RCTs to be causally connected with positive development results. By researching the impact of the intervention on nutrition, language (multilingualism, use of traditional songs and stories), and social network expansion, CanalCanoa measured upstream indicators, thus mixing scientific rigor with an opportunity for innovation and providing important insight and reform of a theory of change.
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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 | 0.062 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".