Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina
Bibliographic record
Abstract
Este artículo aborda la falta de información oportuna sobre la pobreza multidimensional en México. Tres algoritmos de aprendizaje de máquina —la regresión LASSO logística, el bosque aleatorio y las máquinas de vectores de soporte— son entrenados con la ENIGH para encontrar patrones generalizables de pobreza multidimensional en los datos. Los modelos se utilizan para clasificar a cada individuo en la ENOE como pobre o no-pobre para obtener tasas de pobreza trimestrales. Estas estimaciones son más cercanas a los niveles de pobreza multidimensional que la pobreza laboral y brindan una perspectiva precisa sobre la pobreza con más de un año de antelación a la medición oficial.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".