Impacts and Consequences of Victimization, GSS 2004
Bibliographic record
Abstract
According to the GSS, there were over two million violent incidents in Canada in 2004 against persons 15 years of age and over of which one-quarter resulted in an injury. Approximately 24 % of these injurious incidents resulted in the victim seeking medical attention, while for 20 % of incidents victims required bed rest. Slightly less than a third of injurious violent incidents resulted in victims having their day-to-day activities disrupted for a period of one day (31%), while in 27 % of incidents the disruption lasted two to three days. In 18 % of incidents, victims were unable to carry-out their daily activities for more than two weeks. A majority of household and property-related incidents resulted in a loss of under $500 (60%), while for 15 % of incidents losses of more than $1,000 were reported. A majority of incidents impacted victims emotionally (78%), while a minority of incidents did not affect victims at all (21%). Overall, a larger proportion of victims of non-violent incidents felt angry (41%) relative to victims of violent incidents (32%). Regardless of the type of victimization one-fi fth of victims felt upset and expressed confusion and/or frustration as a result of their victimization. Results from the GSS found that a larger proportion of victims of violence (32%) reported sleeping problems than non-victims (17%). In addition, a larger proportion of female victims (37%) of violent victimization reported experiencing sleeping problems relative to their male counterparts (28%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".