Learning alterity in the social economy: the case of the Local Organic Food Co-ops Network in Ontario, Canada
Bibliographic record
Abstract
While the origins of the social economy date long before the period of industrialization or the modern state (Shragge & Fontan, 2000), it is growing in importance as we find ourselves in ‘the cancer stage of capitalism’ (McMurtry, 2013). Facing issues such as exponentially growing inequality, the demise of rural communities, an exploding obesity epidemic and jobless recoveries from repeated financial crises, more and more people are turning to the social economy for solutions to their problems (see McMurtry 2010; Mook et al. 2010). This paper reports on a pilot study that focused on the Local Organic Food Co-ops Network, created by people who oppose the industrial food system and want to specialize in local, organic food. Adopting a political-economy lens to understand this opposition through the words of participants, the study employed semi-structured interviews to explore the learning dimensions of this social economy organization. The study found that respondents participated in social learning and learned alterity in the social economy. The paper concludes that social economy organizations need to prioritize social over economic values, and the potential for change associated with social learning is key to making this choice.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".