Translating global evidence into local practice: The Latin American experience
Bibliographic record
Abstract
Latin America faces urgent challenges in education. In order to succeed, it must tackle insufficient and unequal learning outcomes, low teacher quality, and inadequate public funding, among other problems. Making good use of global evidence is key to reduce these educational gaps in a shorter time span. Moreover, academic research should drive innovations that can make an efficient and effective use of scarce public resources. In this context, 10 Ministries of Education and the Inter-American Development Bank founded SUMMA, the first regional Education Research and Innovation Laboratory for Latin America and the Caribbean. SUMMA aims to synthesize global evidence, contextualizing it to the local context; to generate relevant research, through research networks; and to promote its dissemination and use by governments, schools and teachers. This presentation will present the main achievements and also the main challenges in the quest to transform global evidence into local practices.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".