Socially maladjusted men and their sexual scripts in the context of psychoactive substance use
Bibliographic record
Abstract
Introduction: The research topic was psychoactive substance use as a factor that diversifies the respondents' sexual scripts and as an element of their sexual activity. Material and methods:The data were obtained from research projects carried out among 18-year-aged Voluntary Labour Corps members (N = 563) and prisoners (N = 485).The method used was a diagnostic survey.Results: Adolescent drug use, frequent alcohol consumption, alcohol abuse or early symptoms of alcohol dependence co-occur with more frequent sexual contacts and particularly with risky behaviours such as early sexual initiation (under the age of 15), giving up contraception, prostitution and a large number of sex partners.The majority of men have had multiple sexual experiences while being intoxicated with alcohol or drugs. StreszczenieWprowadzenie: Analizie poddano temat używania substancji psychoaktywnych jako czynnika różnicującego skrypty seksualne badanych oraz jako elementu towarzyszącego ich aktywności seksualnej.Materiał i metody: Dane pochodzą z projektów realizowanych wśród osiemnastoletnich wychowanków Ochotniczych Hufców Pracy (N = 563) oraz więźniów (N = 485).Zastosowano metodę sondażu diagnostycznego.Wyniki: Używanie narkotyków przez adolescentów, częste picie alkoholu, nadużywanie alkoholu lub prezentowanie wczesnych objawów uzależnienia od alkoholu współwystępuje z wyższym odsetkiem podejmujących aktywność seksualną, w szczególności z zachowaniami ryzykownymi -wczesną inicjacją seksualną (przed 15. rokiem życia), rezygnowaniem z antykoncepcji, prostytuowaniem się, dużą liczbą partnerów seksualnych.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".