PILLARS OF YOUTH DRUG ABUSE PREVENTION: PARENTS, POLICE, AND PROJECT DARE (DRUG ABUSE RESISTANCE EDUCATION)
Bibliographic record
Abstract
In 1983 Los Angeles Police Department (LAPD) officials teamed with Los Angeles Unified School District (LAUSD) health curriculum specialist, Dr. Ruth Rich, to redesign an anti-tobacco curriculum, Project Self-Management and Resistance Training (SMART), into Project Drug Abuse Resistance Education (DARE). In the first four years of Project DARE, local, state, and, federal government branches endorsed the program as an efficient tool in the local and national fight against youth drug abuse. Early program evaluations, conducted by the Evaluation and Training Institute (ETI), demonstrated DARE’s ability to change attitudes of students, school faculty, and parents concerning social tolerance of underage drug consumption, while also improving attitudes held toward police officers. These evaluations endorsed the expansion of DARE to all LAUSD schools, followed closely by the program’s nationwide expansion. This thesis examines how police officers displaced parent activists’ role in teaching children drug prevention techniques and why, by 1987, the emergent Bureau of Justice Assistance, a branch of the Department of Justice, funded five Regional Officer Training Centers (RTCs) across the country. By investigating previous federal drug responses, media-driven drug panics, and the expanded role of police in drug prevention education, the DARE program is reconsidered as a curricular vehicle which filtered parental guidance out of youth drug prevention and increased authority and autonomy to police departments across the country.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".