just say what you really think about drugs: cultivating drug literacy through engaged philosophical inquiry (epi)
Bibliographic record
Abstract
Research has shown that “no use” drug education programs, with the objective of scaring or shaming youth into abstinence, have not been effective in addressing problematic substance use. The ineffectiveness of such scare tactic approaches has led program developers to focus on prevention and harm reduction associated with drug use, or in general, health literacy promotion. While significant ‘discussion-based’ drug education programs have been developed over the past decade and has encouraged students to be expressive and critical thinkers regarding drug use, their effective implementation has been a challenge. This paper introduces Engaged Philosophical Inquiry (EPI) as a pedagogical approach in order to promote drug literacy. The EPI approach is used both as the content and means of professional development for high school teachers to address the significant role of teachers in these programs. Its goal is to help teachers become aware of and re-evaluate their biases, beliefs and behaviors, before they are able to facilitate a non-stereotyped, open, and thoughtful discussion on drug use related topics. The overall idea of this paper is based on an in-progress research project sponsored by Mitacs organization. It discusses the significance of the project by first presenting the existing methods and theoretical approaches to drug education. On that basis, it shows how EPI can contribute to traditional drug education approaches. It then describes how the methodology and phases of the project are rooted in a dialogical process that aim for a close collaboration with teachers.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".