Normalization of Racism and Moral Responsibility: Against the Exculpatory Stance
Bibliographic record
Abstract
ABSTRACT In this article, we take the case of racism in contemporary Italy as a starting point for a discussion about moral responsibility for racism in cases where ignorance is involved. We focus on the issue of the normalization of racism and its contribution to different forms of ignorance to assess the extent to which these might potentially mitigate judgments of responsibility for racism, thereby grounding an Exculpatory Stance. After illustrating the phenomenon of the normalization of racism and offering an outline of how the normalization of racism contributes to ignorance, we argue against the Exculpatory Stance by appealing to a socially situated variety of capacitarian approach to the epistemic condition of moral responsibility. This approach provides us with the tools to claim that the moral ignorance favored by the normalization of racism does not mitigate judgments of individual responsibility. Finally, we point out that the interdependence of individual responsibility and the social environment is such that, in addition to backward‐looking individual responsibilities for racism, there are also forward‐looking, both individual and shared, responsibilities to counter racism in its various manifestations and the increased risk of moral ignorance connected to its normalization.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.107 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".