Bibliographic record
Abstract
In the case of domestic violence and sexual abuse, previous studies have linked victim-blaming with victim's feelings of shame, which increases the risk of developing symptoms of depression or PTSD (Bhuptani et al., 2011; Kennedy & Prock, 2016). Other studies have also shown that the more an individual believes in a just world (that the world is fair and that people get what they deserve), the more likely they are to blame victims of domestic or sexual violence instead of the perpetrator (Valor-Segura, Exposito, & Moya, 2011). Our study is a replication of a study completed in Spain in 2011. Valor-Segura, Exposito, & Moya (2011) investigated the influence of beliefs in sexism and beliefs in a just world on judgments of four different situations of domestic violence. Particularly, the study measured how these beliefs across different situations of domestic violence (situations with different causes) may influence the participant to blame the victim instead of the perpetrator. Our replication of Valor-Segura, Exposito, & Moya’s (2011) study follows a similar method. The participants will read a transcript of a call to a help-line where a woman recounts her recent experience of domestic violence. The participants will then be asked a short number of questions to measure whether or not they blame the victim or the perpetrator. Lastly, we will be measuring individuals' belief in a just-world as well as their level of hostile and benevolent sexism through another set of short questionnaires. The main difference in our replication will be that instead of presenting four different scenarios of domestic violence, we will only be presenting one, therefore we will not be looking at how the cause of the domestic violence situation affects victim-blaming. Additionally, our study will be using a sample of adult participants from Canada. We predict that participants holding greater just world-beliefs and hostile sexist beliefs will present a greater degree of victim blaming. We also predict that male participants will blame the victim more than participants who are female (or identify as another gender). Both predictions are informed by Valor-Segura, Exposito, and Moya’s (2011) results. In short, we predict that the original study will be generalizable to a Canadian population. Overall, our study seeks to gain a better understanding of the ideologies associated with victim-blaming. Additionally, it aims to increase the reliability of the original study’s results and see if the original study’s results can be generalizable to a Canadian population and to 2021.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".