Nuevas perspectivas sobre desigualdad y política social en América Latina
Bibliographic record
Abstract
Es ampliamente conocido que América Latina es la región más desigual del mundo (Ábramo, 2019; Lustig, 2020; Sánchez-Ancochea, 2020). En la actualidad, el 10% de la población más rica de la región concentra el 71% de la riqueza, mientras que aproximadamente un tercio de la población vive por debajo del umbral de la pobreza (CEPAL, 2020). La pandemia del COVID-19 no sólo profundizó la desigualdad, sino que dejó en evidencia las enormes diferencias entre territorios, etnias, géneros y ocupaciones sociales en aspectos tan diversos como acceso a internet, servicios de salud, agua potable en casa y seguridad alimentaria. Varios factores estructurales configuran este crudo panorama de las múltiples desigualdades: los legados de la estructura colonial que operaron en contra de afrodescendientes e indígenas (Acemoglu et al., 2001); la economía basada en la explotación de materias primas (Williamson, 2015; Ocampo, 2017; Frankema, 2009); las limitaciones de los Estados en la provisión de servicios públicos de buena calidad (Otero-Bahamón, 2020); democratizaciones incompletas (Acemoglu y Robinson, 2006; Boix, 2003); la sobresaliente capacidad de determinados grupos de poder de moldear la política pública (Fairfield, 2015; García-Montoya, 2020); políticas sociales segmentadas (Pribble, 2013; Garay, 2017); así como dinámicas de corrupción y clientelismo (Berens, 2021), entre otros.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".