Cooperative Economics — with Elvy Del Bianco
Bibliographic record
Abstract
Elvy Del Bianco of Vancity Credit Union speaks to the power of cooperatives to help communities meet their own needs. He is in conversation with host Am Johal about the role coops can play in the production of social goods, and how they can support communities of entrepreneurs, small businesses, non-profits, and workers through solidarity and infrastructure. They also discuss the Vancity Emilia-Romagna Co-operative Study Tour and how this particular area of northern Italy has what Elvy calls, “the most significant cooperative economy on earth.” It’s also a region that sees some of the lowest poverty rates and highest levels of civic engagement in Italy. Am and Elvy discuss how similar cooperative policies and models could be implemented in BC and Vancouver to bolster communities, address precarious work, and deliver much needed services.\nElvezio (“Elvy”) Del Bianco is a cooperative enterprise educator, developer, financier and advocate. He coordinates Vancity’s support for new and established cooperatives, co-founded and organizes the Cooperate Now co-op business boot camp, co-authored the “Seven Ways to Grow BC’s Co-op Sector” policy document, and works to build out the infrastructure to support new cooperative enterprises.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".