How Does Gender, Relationship, and Emergency Situation Affect Perceptions of Intentionality When a Child is Harmed?
Bibliographic record
Abstract
This study will expand on a previous pilot study to explore to what extent the caregiver-child relationship and the situation impact perceptions that a crime was committed when a caregiver calls 911 for a child in need of emergency medical aid. However, this study will take the research one step further by not only manipulating the relationship and the situation, but also the gender of the caregiver. To do this, we will be using four of the five vignette situations and four of the five caregiver-child relationships based on the results of our pilot testing (also pre-registered under this same project) but will be newly adding the manipulation of the caregiver gender. The four vignette situations will fall into two categories based on the nature of actual cases in the Canadian Registry of Wrongful Convictions (sick child situations vs. sudden emergency situations), with two vignettes for each category for stimuli sampling (sickness child vignettes: child died in their bed and child with a history of medical issues died in their crib; sudden emergency vignettes: child with a seizure tested positive for cocaine and children died in a house fire). Additionally, the caregiver-child relationships will also be split into two categories, with two examples each for stimuli sampling: parental (parents and stepparents) vs. babysitters (family friend babysitting and family member babysitting). Participants will be randomly assigned to see two vignettes so that each variable (vignette situation, caregiver-child relationship, gender of caregiver) is manipulated within-participants, with all combinations of the variables and order of presentation counterbalanced across participants. Participants will read one vignette and will then be asked questions about a) violation of expectations, b) moral agency, c) suspiciousness, d) willingness to treat the caregiver as a suspect, and e) intentionality of harm (a-d items were all in pilot testing study as well, but e items will be new to this study). They will then be asked to read the second vignette and will answer the same questions. After answering all these questions for each vignette, participants will also be asked questions regarding perceptions of the degree to which men and women spend time raising children and/or are responsible for handling emergency situations involving children (these items were modified from the pilot testing study so that instead of responding to each question for various relationships to the child, as was done in the pilot testing study, they will just be asked to answer the items for ‘males’ and ‘females’).
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".