Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Howard Becker's analysis of marijuana use has had long-standing impacts upon our collective understanding of how individuals become drug users. This paper ultimately asks whether the framework described by Becker is unique to recreational marijuana use or, rather, a process that is not fundamentally different from that employed with the consumption of any psychoactive drug, whether taken for medical and/or recreational purposes. We used detailed semistructured interviews with Canadians (n = 22) who self-identify as medical marijuana users. Respondents were asked a series of questions about their reasons for use, medical conditions and symptoms, current and past consumption habits, how they learned about medical marijuana, and the substance of that learning process. The analytic approach is informed by Becker's conceptual framework and peer-reviewed and publicly available information sources. Although the principal reasons for self-described medical use—relief from pain, anxiety, and insomnia—are consistent across respondents, the way in which they come to define their use as medical is heterogeneous. Sources of information and the substance of such information are more complex and detailed than that described by Becker, suggesting a more intricate learning process when the motivation for use is therapeutic. Drawing upon detailed interviews with self-described medical users, we argue that the line drawn between recreational marijuana use and medically driven use is blurred: Most self-described users are seeking both relief from pain and the pursuit of recreation in their use of the drug, a finding that has implications for the logic of a clear separation in law and policy between these two motivations for consumption.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it