Canadian Cannabis Industry and Social Responsibility: The Case of Canopy Growth
Bibliographic record
Abstract
Through the ethnographic research developed primarily in the city of Smiths Falls, Ontario and desk review that included the consultation of digital platforms and the problematization of the concept of social responsibility, the objective is to reflect on the relationship between the corporate discourse of Canopy Growth and its social responsibility practices. The article is made up of different sections; first, it reflects on the incipient literature and its contradictions regarding cannabis through the lenses of various disciplines, as well as on the historical development of the concept of corporate social responsibility within capitalism, subsequently it identifies narratives regarding social responsibility promoted by Canopy Growth in its press releases and other digital inputs and some counter-narratives issued by other stakeholders. Finally, the narratives of members of the interest groups are presented, giving prominence to former Canopy Growth workers who, share their first-hand experiences in the corporation. The conclusions demonstrate the multiple inconsistencies that Canopy Growth embodies when raising the concept of social responsibility in its media discourses. These were evidenced by personnel who worked for the corporation in the city of Smiths Falls, Ontario. Knowing the modus operandi of a leading corporation in the cannabis industry gives us the tools that are necessary to envision its potential actions in Mexico given that Canopy Growth has shown an interest in expanding into our territory for several years.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.044 | 0.021 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".