Big data y acceso a la información en México
Bibliographic record
Abstract
Berliner, Daniel, Brian Palmer-Rubin, Jésica E. Tapia Reyes, Benjamin E. Bagozzi, Aaron Erlich. (2022). Big data y acceso a la información en México (Informe de la Política Pública). The London School of Economics and Political Science. URL: bigdataytransparenciamx.lse.ac.uk/• El uso de la información ha crecido con el tiempo en respuesta a una gran variedadde necesidades.• Mayor profesionalización de ciudadanos y miembros de la sociedad civil que la solicitan; desarrollo de un ecosistema de expertos en transparencia y rendición de cuentas.• Aumento de respuestas engañosas por parte de los sujetos obligados, sobre todo para los solicitantes no expertos.• Sugerencia de política pública: mejorar el sistema para rastrear los textos de las respuestas en formatos más accesibles.• Sugerencia de política pública: mejorar la capacitación en las Unidades de Transparencia para estandarizar las prácticas para contestar a solicitudes de información pública.• Sugerencia de política pública: fortalecer los lazos del INAI con las organizaciones de la sociedad civil y enlistar a estas últimas como intermediarias para apoyar el uso ciudadano del sistema.• Para más información, ver la página web del proyecto: bigdataytransparenciamx.lse.ac.uk/
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".