Bibliographic record
Abstract
The Synergia Project is a new initiative whose purpose is to promote online learning technology to support the diffusion of co-operative knowledge (both formal and tacit) and practice to meet the challenges of building a sustainable, equitable, and socially just future. A key aim of the project is to bridge the international co-operative movement with the emerging movement for a new commons and the global movement for sustainability. \nHoused at the Athabasca University (AU) and supported by the participation of British Columbia-Alberta Social Economy Research Alliance (BALTA) in Canada, Co-operatives UK, Schumacher College, and the P2P Foundation, the project commenced in December 2013 with a dialogue process that engages key innovators and experts from the co-op, new commons and sustainability fields to participate in the design and content of an online learning platform using MOOC technology. The project is funded by the AU Research Fund. \nThe completion of the program design and the confirmation of the MOOC content comprise Stage 1 of the project. Stage 2 entails the completion of the MOOC “curriculum” and the launch of the Synergia MOOC with links to concrete development opportunities on the ground. \nThe project is being co-ordinated by Mike Gismondi at the Centre for Social Science at AU. John Restakis, Research Associate with Co-operatives UK and Research Investigator with the FLOK Project, is Lead Researcher for the Project and Pat Conaty, Research Associate with Co-operatives UK, is the Research Lead for the UK.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.298 | 0.163 |
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".