Ciclo de elecciones de América Latina (enero-junio 2023)
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
Ciclo de elecciones de América Latina (enero-junio 2023) Latin American election cycle (January-June 2023) Autores Narda Carranza Oficina Nacional de Procesos Electorales, Lima, Perú DOI: https://doi.org/10.53557/elecciones.2023.v22n25.13 PDF Detalles Estadísticas Cómo citar Licencia Publicado el 30 junio 2023 Número: Vol. 22 Núm. 25 (2023): Enero - Junio Sección: Ciclo de elecciones Compartir: Z Carranza, N. (2023). Ciclo de elecciones de América Latina (enero-junio 2023). Revista Elecciones, 22(25), 371–373. https://doi.org/10.53557/elecciones.2023.v22n25.13 Más formatos de cita ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver Descargar cita Endnote/Zotero/Mendeley (RIS) BibTeX Derechos de autor 2023 Revista Elecciones Esta obra se encuentra bajo una licencia Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) 4.0 International License. PlumX Dimensions Vista del artículo: Total Resumen PDF 23 17 6 Descargas: Los datos de descargas todavía no están disponibles.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.011 |
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