Conceptual Framework for the Purpose of Measurement of Cooperatives and its Operationalization
Bibliographic record
Abstract
This report presents a general overview of how cooperatives and key concepts are measured for statistical purposes. It is based on extant work and literature. The first part of the report reviews the most important statistical studies on coopera-tives. Conceptual and methodological issues are identified concerning the identification and classification of cooperatives, including a discussion about core components and boundary areas. The report then proposes a conceptual framework for defining and classifying cooperatives for measurement purposes. This framework suggests a set of four structural-operational qualification criteria to identify cooperatives. It also proposes a double classification of cooperatives, based on the main economic activity and on a characteristic helping to distinguish types of cooperatives. The report also raises some issues about the measurement of membership and value added, as well as employment in cooperatives. On these aspects, the report concludes that focusing on membership rather than on measuring individual members may be the best path. To measure the economic contribution of cooperatives referring to the concept of value added is not recommended unless it is adapted to cooperatives. Other modes of calculation of the cooperative’s economic contribution will need to be explored. The nature of employment in cooperatives will also need to be reflected accurately in overall employment statistics.
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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 | 0.038 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".