Translating global evidence into local practice: The Latin American experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it