18-24 year olds: Normal Risks Takers? Jamie WiebeOutline! Substance Use and Abuse Literature! General Prevalence Study (Ontario) (n=5, 000)! Detailed self reports (n=20) From the LiteratureSubstance Use Literature
Bibliographic record
Abstract
! Periods of highest use of alcohol and marijuana peak from ages 19 to 21, and then starts to decline (Chen & Kandel, 1995)! From age 20 to 29 a general trend toward lower levels of substance use among individuals and their friends. Most pronounced among those who are married or become parents (Labouvie, 1996) Substance Use Literature cont...! Marriage and parenthood reduce the risk of developing alcohol disorder in young adulthood (Chilcoat & Breslau, 1996)! Individuals moderate alcohol consumption prior to the actual transition to married status, with the trend continuing in the first year of marriage (Miller-Tutzauer et al., 1991) Substance Use Literature cont...! Cessation of illicit drug use is influenced by getting married and having children.! Factors important for persistence of illicit drug use in adulthood parallel factors important for initiation of use in adolescence: friends favorable to use and lack of
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".