Reseña al libro de Pierre Dardot, La memoria del futuro: Chile 2019-2022
Bibliographic record
Abstract
Datos del autor: Pierre Dardot es doctor en Filosofía, profesor emérito de la Universidad de Nanterre (Paris-X) e investigador adjunto del laboratorio Sophiapol, de la Unidad de Investigación en Sociología, Filosofía y Antropología Política de la misma universidad. Junto a Christian Laval fundaron y lideran el grupo de estudio Question Marx. El profesor Dardot es especialista en Marx y Hegel, y sus trabajos se inscriben en el horizonte de la crítica política de la sociedad contemporánea, profundizando, principalmente, en cuatro líneas de investigación: el pensamiento de Karl Marx; el análisis del neoliberalismo; Común, como una indagación en las alternativas al neoliberalismo; y la soberanía estatal en Occidente. En esta última línea de trabajo es donde podemos enmarcar el libro reseñado.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".