What does it mean to be responsible for Canadian Cannabis firms? An examination of CSR identity through social media disclosure
Bibliographic record
Abstract
Purpose The study aims to examine the extent of the corporate social responsibility (CSR) disclosure of Canadian cannabis firms and how they view responsibility. It also explores how cannabis firms build their CSR-based organizational identity through Twitter. Design/methodology/approach Deductive and inductive content analyses were carried through on tweets for a sample of 18 firms listed on the Canadian marijuana index during the legalization period of the recreational use of cannabis. Findings The results of this study show that cannabis firms approach responsibility by focusing on consumer and community/local development and by raising awareness and providing product information. The findings also highlight that the firms build their organizational identity mainly around their products’ medical benefits, the scientific efforts behind product development and the continual stigmatization they experience. At the industry level, cannabis firms attempt to build a harmonized identity to neutralize stigma. Originality/value This study allowed for a comprehensive understanding on how cannabis firms position themselves within an emergent sin industry and how they create their CSR identity through Twitter. It advances our understanding on the meaning of responsibility about the specific and distinctive features of the cannabis industry. From the methodology side, this study developed two content analysis tools: a coding instrument and a dictionary. These tools could be useful for conducting future studies related to the CSR disclosure of cannabis firms worldwide.
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.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".