DUAL PROCESS THEORY AND VICTIM BLAMING BEHAVIOUR: AN EXPLORATION OF COGNITIVE MECHANISMS UNDERLYING BLAME EVALUATIONS TOWARD INNOCENT VICTIMS
Bibliographic record
Abstract
This thesis explores the cognitive mechanisms underlying victim-blaming behaviour. Some past researchers posit that victim-blaming behaviour requires deliberative thought (van den Bos & Maas, 2009), whereas others suggest that it occurs automatically (Harvey, Callan, & Matthews, 2014). The initial aim of this experiment was to explore whether deliberative thought modulates victim-blaming decisions toward innocent victims of illness or injury. The secondary aim of this experiment was to explore whether a cognitive conflict between one’s belief in a Just World (Lerner, 1977) and presented stimuli might modulate victim-blaming decisions. However, after extensive pilot testing, we were unable to produce the expected Just World findings that high-suffering victims should elicit more blame than low-suffering victims. As one of our key theoretical tenets was not supported by our results, this thesis was revised to proceed as an exploratory cognitive investigation of blame to passages that appear to generate high and low levels of victim blame responses while under full working memory resource capacity. Under this narrowed theoretical lens, our research questions were revised to explore the involvement of deliberative thought in producing victim-blaming decisions, and the possible role of an unknown cognitive conflict in modulating victim blaming behaviour within the passages presented. To explore these possibilities, we collected participants’ blame and metacognitive experience of Feeling of Rightness (FOR; Thompson & Newman, 2017) from 200 English-speaking respondents to each a high-suffering and low-suffering victim. Blame responses were collected under high and low levels of time pressure to selectively restrict or grant access to available working memory resources necessary for deliberative thought. Our findings revealed that blame responses can be lowered in both high and low victim-suffering conditions when time pressure is alleviated. Thus, our findings support the possibility that deliberative thought modulates victim-blaming decisions. We also found that a lower FOR and longer rethinking times preceded a change in Time 2 responding. Thus, our findings imply the involvement of a possible cognitive conflict in modulating victim blame responses. Implications to Just World Theory are discussed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".