Which Fair Trade principles travel to distant sectors? An analysis of social and sustainability enterprises and entrepreneurship in the legal cannabis (marijuana) sector
Bibliographic record
Abstract
Abstract Social enterprises, social entrepreneurship and sustainable business models are increasingly common in sectors where Fair Trade does not have a strong presence (e.g. mobile phones and software). This research asks: To what extent do social and sustainability enterprises and entrepreneurship (SSEEs) in these ‘distant’ sectors engage the principles of Fair Trade? It draws on an in-depth, multi-method case study of SSEEs in the legal cannabis sector in Portland, Oregon, US. It analyzes data from magazine advertisements, public and industry events, and visits to 85 cannabis retailers. The results suggest that SSEEs in distant sectors may not be engaging some of the principles that are at the heart of Fair Trade. These include transparency, accountability, collaborative price-setting, pre-payment, honouring contracts, inclusive governance and worker organizing. SSEEs appear more engaged with the environment and buy-cotting (privileging) small producers, sustainable businesses and marginalized groups. How can Fair Trade encourage and empower SSEEs in distant sectors to engage more principles of Fair Trade?
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".