Deberes de caridad, deberes de justicia y la creación del otro en la tradición kantiana
Bibliographic record
Abstract
Este artículo sostiene que hay problemas importantes en la tradición kantiana, en la medida en que existe una tendencia persistente a atribuir valor intrínseco basado en la existencia de capacidades cognitivamente relevantes. Además, se argumenta que después de un análisis de la obra de Kant, la exclusión de seres moralmente relevantes es una inclinación prevalente de la tradición. El trabajo de Rawls es utilizado para mostrar cómo, del racismo, el proyecto kantiano pasa al capacitismo. En la conclusión, se demuestra que incluso una versión más contemporánea y abierta de la tradición kantiana, como la defendida por Korsgaard, sigue vinculada a los mismos problemas que las formulaciones anteriores de la tradición. In this paper, there are important problems in the Kantian tradition that are argued, insofar as there is a persistent tendency to attribute intrinsic value based on the existence of cognitively distinct capacities. Furthermore, it is argued that after an analysis of the work of Kant the exclusion of morally relevant beings is a prevalent inclination of the tradition. The work of Rawls is used to show how from racism, the project moves on to ableism. In the conclusions, is showed that even a more contemporary and open version of the Kantian tradition, such as the one defended by Korsgaard, is still bound to the same problems as the previous formulations of the tradition.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".