Bibliographic record
Abstract
The article analyses the discussion of cannabis regulation in the Estonian media. In the past five years, there has been a noticeable shift in discussion of drug policies in some Western countries and regions (the US, Canada, Latin America, etc.) from a punitive focus towards a more liberal approach. The Global Commission on Drug Policy recommends that countries put an end to civil and criminal penalties for drug use and possession. In this context, the article examines how the Estonian press has reacted to the situation. Which approach to cannabis (continuing to ban it vs. advocating legalisation) prevails in opinion pieces? What are the main arguments both for and against its legalisation? The media could play a prominent role in determining public opinion about illicit drugs and shaping relevant public policies. Hence, the author looks also at how the coverage has changed over time. A content analysis of 57 opinion articles, editorials, comments, interviews, and summaries of public speeches was carried out to study the political debate surrounding cannabis in 2009 and 2015, both years in which it was high on the media agenda. The content analysis was complemented by the method of close reading. The findings indicate that press coverage of cannabis has become more tolerant towards ‘softer’ drug policies. The chorus of ‘voices’ has become more complex, which reflects development of the drug-politics discourse. While the 2009 debate was launched by pro-legalisation lawyers and the discussion involved various professional experts (among them medical doctors, lawyers, and specialists in drug prevention), cannabis more often made headlines in 2015 because of work by civil activists, columnists, writers, etc. A strong dichotomy between traditional law-enforcement discourse and cannabis-legalisation and harm-reduction discourses has emerged. The author expresses the opinion that a shift in the global drug-policy debate alongside softened media coverage may pave the way for changes in the national drug policy.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".