Legalisation of Cannabis In Finland : Analysis of the Economic, Societal and Fiscal Impacts
Bibliographic record
Abstract
This thesis will dive into the economic, societal and fiscal impact of the legalisation of Cannabis in the Nordic country of Finland. By looking into other countries with similar economic, environmental, and societal aspects that have already gone through the process of legalisation, such as Canada, Thailand, and Uruguay. We can identify the trends that occurred after the legalisation process as well as the different structures implemented in order to regulate the negative effects of cannabis legalisation. Furthermore, by uncovering Finland's current economic position we can determine whether it would be logical and profitable to proceed with the legalisation of Cannabis. First and foremost, a proper non-stigmatized understanding of what Cannabis is and how it can be used must be established in order to have an effective and unbiased judgement of the natural substance referred to as Cannabis. Furthermore, tax frameworks will be considered as it is a part of the process of legalisation, as well as different regulations in order to control and effectively regulate the impact legalisation has on society. The results of this research paper have found that the legalisation of Cannabis provides a substantial potential benefit for Finland as it would open up a whole new industry as well as economically the tax revenue gained from this new industry would be used to tackle the limited negative impact that legalisation would have on society as well as potential help tackle other societal challenges Finland faces such as overconsumption of alcohol.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".