Bibliographic record
Abstract
Victim blaming is a harmful but quite pervasive phenomenon occurring in contemporary societies. When people engage in victim blaming, they shift the burden of the harmful act from the perpetrators and place it upon the victims instead.This article explores how the discourse on moral responsibility can help make sense of victim blaming. The distinction between moral responsibility and blameworthiness can shed light on the contradictory intuitions that people experience when they hear about a victim who took what seems to be an unnecessary risk. The focus of this article is to explain these intuitions and respond to them by suggesting that victims not only are not blameworthy when they take risks that challenge specific norms but instead are praiseworthy. Finally, whether such risks are justified when the agents taking them have people dependent upon them is discussed. Attending to structural injustice can point out why some choices seem more justified than others. Victims who take justified risks are praiseworthy, even when their efforts do not produce significant results. This article aims to address the absence of victim blaming in discussions of moral responsibility and to bring philosophical attention to this issue. The goal is to disentangle the phenomenon of victim blaming while supporting victims and vulnerable groups.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| 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".