“Cuando los algoritmos se apoderan de la administración y políticas públicas, el potencial de daño es ilimitado”
Bibliographic record
Abstract
Entrevista con Catherine (“Cathy”) Helen O’Neil, nacida en los Estados Unidos, conocida mundialmente por sus estudios críticos sobre los efectos negativos de los algoritmos. Doctora en Matemática por la Universidad de Harvard, es autora de los libros Haciendo Ciencia de Datos (2013), Armas de Destrucción Matemática (2016) y La máquina de la vergüenza (2022). Luego de trabajar para el sector financiero como científica de datos, puso en marcha ORCAA, una empresa de auditoría algorítmica. Es colaboradora habitual de la agencia de noticias Bloomberg Opinion, autora del blog y miembro del Laboratorio Tecnológico de Interés Público de la Escuela de Gobierno John F. Kennedy de la Universidad de Harvard.
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.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 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".