Marijuana as scapegoat, cannabis as medicine : a cognitive-rhetorical analysis of a Canadian drug-policy problem
Bibliographic record
Abstract
This thesis examines the remarkable ambivalence towards Cannabis sativa L. in Canada, evidenced in the high-stakes contest between competing public conceptions of, and private interests in, the drug-plant and cash crop. Official policy regarding the enigmatic substance over the first decade of the 21st century has been notably erratic, and during this period a number of dramatic shifts in Canada’s administrative and clinical approaches to cannabis have occurred. This has resulted in changes which stand out significantly in the history of the plant’s medicinal, recreational, and industrial use in this country. Despite the recent surge in acceptance and legitimacy of its medical use in a number of jurisdictions, the definition, classification, regulation, prescription, cultivation, marketing, and consumption of cannabis for therapeutic purposes continue to pose, for many groups and individuals in this country, a medico-legal dilemma—with the boundary between licit and illicit a blurry one in deed, and in word. The many lingering questions about proper ethical and practical conduct within (and parallel to) the framework of the MMAR have made it exceedingly difficult for many participants to arrive at a comfortable fit between the activities pursuant to their roles and the uncertain, unqualifiable, or unappreciated value (or risk) entailed by those roles. I intend not only to improve understanding of the rhetorical, linguistic, and socio-cognitive basis of a particular drug-policy problem, but also to demonstrate, in so doing, the broad analytical reach of rhetorical theory and criticism, and the usefulness of applying rhetorical and cognitive-linguistic methodologies together. Through analysis of suasive elements of key terms and conceptual structures in the discourse, and of differently motivated role-value connections assumed by participants therein, I forward the claim that marijuana has played the part of the scapegoat in medicine and, more broadly, among all drugs.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.037 | 0.040 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| 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".