Scaling Up: The Convergence of Social Economy and Sustainability - Sellsheet
Bibliographic record
Abstract
The book presented in this sellsheet and recently published by Athabasca University Press examines the potential of the social economy to transform the systems that make our current ways of life unsustainable. The book draws extensively from research conducted by members of the BC-Alberta Social Economy Research Alliance (BALTA) and others over the 2006-2012 period. It is co-edited by BALTA researchers: Dr. Michael Gismondi of Athabasca University, Dr. Mary Beckie of the University of Alberta, Dr. Sean Connelly of Otago University (New Zealand) and Dr. Mark Roseland of Simon Fraser University. Other BALTA associated researchers who contributed chapters were: John Restakis of the B.C. Co-operative Association, Dr. Julie MacArthur of the University of Auckland, Dr. Lynda Ross and Juanita Marois of Athabasca University, George Penfold and Terri MacDonald of Selkirk College, Noel Keough and Erin Swift-Leppäkumpu of the University of Calgary, Sean Markey and Freya Kristensen of Simon Fraser University, and Stewart Perry of the Canadian Centre for Community Renewal.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".