Presented at Co-operative Innovation: Influencing the Social Economy, sponsored by the Canadian Association for Studies in Co-operation (CASC),
Bibliographic record
Abstract
This afternoon, I will argue that, by supporting the fair trade movement, the co-operative movement can also help itself. I will examine survey evidence regarding consumer support for FT and for co-operatives, report on co-operatives ’ involvement in fair trade and touch on information from interviews I did with Canadian fair trade practitioners. What is fair trade? Casual observers may equate fair trade with a fair price, but that is only one aspect of the fair trade project. Raynolds (2002) suggests that the fair trade movement “critiques conventional production, trade and consumption relations and seeks to create new more egalitarian commodity networks linking consumers in the global North with marginalized producers in the global South ” (p. 404). Furthermore, she argues that fair trade’s “true significance lies in... its ability to create new links between producers 2 and consumers ” (p. 404). This, I think, demonstrates that there is a significant overlap between the objectives of the fair trade and co-operative movements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".