Community Action Research: Who Does What to Whom and Why? Lessons Learned from Local Prevention Efforts (International Experiences)
Bibliographic record
Abstract
This paper describes lessons learned about community action research, drawing upon papers written and presented at a recent international conference on community action research and the prevention of alcohol and other drug problems. Projects reflected both action and evaluation research traditions and focused on a variety of issues from moderation of drinking to alcohol-related violence, and on range of target populations from youth to specific ethnic groups. The interventions described ranged from policy-based prevention to education and training and to secondary prevention and treatment. Lessons identified in the papers are discussed within three broad areas: the community targeted for change; the implementation of community projects; and community action research projects generally. The common lessons emerging from these diverse projects provide useful lessons on which to base future progress in community action research.
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 | 0.077 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".