Racialización, racialismo y racismo: un discernimiento necesario
Bibliographic record
Abstract
Este articulo pretende arrojar luz sobre las diferencias y conexiones intimas entre los terminos racializacion, racialismo y racismo. Con ello, busca ofrecer algo de claridad sobre formas de pensar y operar poco repasadas dentro de las politicas del anti-racismo. El orden en el que este analisis se articula, se detiene primero en definir el concepto matriz de «racializacion», sus acepciones y las consecuencias politicas y epistemicas de su contenido. Posteriormente, el texto se dedica a analizar el poco discutido termino racialismo. En una tercera seccion los esfuerzos de este analisis se concentran en examinar el concepto de «racismo» y en describir las estrechas relaciones que este tiene con los conceptos anteriores. A modo de conclusion, se reflexiona sobre la utilidad de este ejercicio de discernimiento para las politicas del anti-racismo. Palabras claves: Racializacion, racialismo, racismo, politicas del anti-racismo. Abstract: The present paper aims at shedding light on differences and intimate connections of terms racialization, racialism, and racism. Through it, it tries to clarify the not very much assessed ways of thinking and operating, within antiracism policies. The order in which the analysis is articulated, it first defines the main concept of ´racialization´, its meanings and political and epistemic consequences of its content. Then the text applies itself to analyze the little assessed term racialism. In a third section, the efforts of this analysis are concentrated on examining the concept of racism and describing the close relations it has with the previous concepts. As a conclusion, this essay argues upon the usefulness of the exercise to distinguish the antiracist policies. Keywords: Racialization, racialism, racism, antiracist policies
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".