GUN AVAILBILITY AND USE OF GUNS FOR MURDER AND SUICIDE IN CANADA: A REPLICATION '
Bibliographic record
Abstract
Stimmay.-Following Lester, the rate of accidental death from firearms was asso-ciated with the percentage of firearms used in suicides and on homicide victims ex-cept for persons aged 55+ yr. One proxy measure of the availability of firearms in a society might be the rate of accidental death from firearms (Cook, 1982). Lester (2000) found that the rate of accidental death from firearms in Canada from 1970-1995 was positively associated with both the rates for homicide and suicide from firearms and significantly so for rate of accidental death from firearms and rate of homicide from firearms. Later, Lester (2001) reported that the rate of accidental death from firearms in Canada was significantly and positively associated with (1) the percentage of homicide victims killed by firearms for the total population, men, women, and those in five age groups for 1974-1995 but not for those aged 55+ yr. and (2) the percentage of suicides using firearms for the total population, and those in four age groups for 1970-1995 but not for those aged 55+ yr. The present research replicated Lester's (2001) study and added data for the years 19961998. The data for the rate of accidental death from firearms for the years 1996-1998 were calculated
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".