One size (doesn’t) fit all: new metaphors for and practices of scaling from indigenous peoples of the Northwest Amazon
Bibliographic record
Abstract
Ten years of field research and collaborative development of programs for early childhood in the Upper Rio Negro region of the Amazon provide the authors with new metaphors for achieving wider social impact and new frames to add to the international debate on 'scaling' social change initiatives. Using anthropology and ethno-ontology to think questions of universal and particular, center and periphery, the article reflects on the dangers of monolithic scaling to cultural diversity and future innovation. Instead of the metaphor of scaling - adopted in the discourse of public policy and international development from the Fordist or Taylorist efficiency of the economy of scale - indigenous people speak of exchange, sharing, and transformation. These ideas seek to connect local and decolonized models and value the diversity of local knowledges, epistemologies, and practices around early childhood development. Based on the expansion of the CanalCanoa project among diverse indigenous communities, the paper proposes a flexible and bottom-up model of achieving impact at scale through empowering local actors to teach each other and establish local criteria of learning and evaluation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".