La pobreza en Ecuador a trav?s del ?ndice P de Amartya Sen
Bibliographic record
Abstract
La presente investigaci?n realiza un an?lisis de la pobreza en Ecuador en el periodo 2006-2012 a nivel nacional y provincial, a partir de la aplicaci?n del ?ndice de la Pobreza (P) propuesto por Amartya Sen (1976), ya que dicho ?ndice permite evaluar las condiciones de pobreza al integrar la tasa de incidencia de la pobreza, la brecha de ingresos de los pobres y el coeficiente de Gini de los pobres en un ?ndice compuesto que entrega informaci?n de la profundidad de la pobreza y la desigualdad de ingresos entre los pobres. Los principales resultados que aporta la investigaci?n son que en el a?o 2012 a nivel nacional la pobreza fue menos profunda que en el a?o 2006, sin embargo la desigualdad entre los pobres aument? ligeramente; y a nivel provincial en quince provincias se evidenci? mejoras en sus condiciones de pobreza, mientras que seis agravaron esta condici?n. As? mismo se evidencia que ser hombre o mujer no es una condicionante para ser pobre. Paralelo a esto se evidencia que el ?rea y la regi?n donde se viva, la etnia, el nivel de instrucci?n, la condici?n de actividad y el tama?o de los hogares se relacionan directamente con la probabilidad de ser pobre. Finalmente se evidencia que disminuir las tasas de subempleo en el pa?s mejora las condiciones de pobreza.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".