Evidence for a Big Brother Effect in Survey-Based Fear of Crime Research
Bibliographic record
Abstract
The objective of this study was to determine whether sibling sex and birth order have any influence on individuals' reported fear of crime levels. Based on literature relating to gender, socialization, vicarious fear for spouses and children, and sibling influence, three hypotheses were formed. It was expected that a) having siblings would be protective against fear, b) male fear of crime would increase with the number of younger sisters and c) female fear of crime would decrease with the number of older brothers. A total of 83 McMaster University undergraduate students completed a survey that included demographic questions and a fear of crime index. Results indicated the existence of a "big brother effect", whereby females with older brothers exhibited less fear of crime than other females. There was no statistically significant difference in fear of crime among those with and without siblings and no sex-specific sibling effects on fear of crime in males. Explanations of this result focused on female vulnerability, socialization and the particular influence of older brothers on their sisters' behaviour and characteristics. This study highlights the influence of siblings on fear of crime and provides impetus for future research
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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 | 0.027 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".