Experimental evidence of subtle victim blame in the absence of explicit blame
Bibliographic record
Abstract
We argue that people will often eschew explicit victim blame (e.g., claiming that "X is to blame") because it is counternormative and socially undesirable, yet they might still engage in subtle victim blame by attributing victims' suffering to behaviors the victims can control (i.e., "high control causes"). We found support for this argument in three online studies with US residents. In Studies 1 and 2, participants viewed a victim posing either a high threat to the need to believe in a just world, which should heighten the motivation to engage in victim blame, or a low threat. They then rated explicit blame items and attributions for the victim's suffering. Explicit blame was low overall and not influenced by victim threat. However, participants attributed the high threat victim's suffering, more than the low threat victim's suffering, to high control causes, thus showing a subtle blame effect. In Study 2, explicit blame and subtle blame were less strongly associated (in the high threat condition) for individuals high in socially desirable responding. These results are consistent with our argument that explicit and subtle blame diverge in part due to social desirability concerns. In Study 3, most participants believed others viewed the explicit blame items, but not the attribution items, as assessing blame. Thus, attributions to high control causes can be seen as "subtle" in the sense that people believe others will view such statements as reflecting constructs other than blame. Our studies suggest a way of responding to innocent victims that could be particularly relevant in a modern context, given increasing social undesirability of various negative responses to disadvantaged and victimized individuals.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".