Bibliographic record
Abstract
The chapter elaborates on the strategic role of performance indicators in support of transformation to sustainable socio-ecological-economic systems. Given the purpose and identity of cooperatives, their impact on fair income distribution, promotion of economic democracy, and de-commodification of necessities and fictitious commodities (Polanyi, K. (Polanyi, K. (1944 [2001]). The Great Transformation. Farrar & Rinehart [Beacon Press]. [2001]). The Great Transformation. Farrar & Rinehart [Beacon Press].), we consider their contributions to the requisite radical imagination and the different institutional logic needed for a transformative change of the system. The intersection between the cooperative model and the Economy for the Common Good (ECG) approach to measures of performance is examined as an example of values-based indicators. The chapter points out the need to use context-based indicators with thresholds and allocations (McElroy, M. (McElroy, M. (2015). Science- vs. Context-Based Metrics – What’s the Difference? Sustainable Brands. https://sustainablebrands.com/read/new-metrics/science-vs-context-based-metrics-what-s-the-difference). Science- vs. Context-Based Metrics – What’s the Difference? Sustainable Brands. https://sustainablebrands.com/read/new-metrics/science-vs-context-based-metrics-what-s-the-difference ), in order to appropriately assess sustainability. It also proposes that cooperative goals and purpose can set the sustainability benchmarks for social indicators.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".