“The Fuzzy Feeling Isn’t There”: Version One of the Cooperative PerformanceIndicator Tool Misses the Mark for Micro Coops, Eric M. Gosselin
Bibliographic record
Abstract
This study test-ran the newly developed Cooperative Performance Indicator (CPI) tool on three community bicycle shop cooperatives (Bike Coops) in order to examine its applicability to micro, nonprofit coops.Three Bike Coop directors were individually interviewed in a semi-structured fashion while they were completing the CPI tool.The respondents are located in three different regions of Canada: the Prairies, Québec and Atlantic Canada.The analysis suggests that the CPI tool is inadequate at measuring the performance of coops of all sizes.The tool misses the mark when it comes to Bike Coops which are micro, non-profit coops.First, the CPI pilot project participants (PPPs) facilitated by the Centre of Excellence in Accounting and Reporting for Cooperatives (CEARC) will need to rethink the questions in the CPI tool to make them more applicable to micro, non-profit coops like Bike Coops.Second, community Bike Coops need to start tracking more data based on cooperative principles.The CPI tool has not yet been made public; therefore, this research is the first to gather qualitative data from coop actors who are not part of the PPPs involved in the development of the CPI tool.Eric Gosselin embarked on a career in the music business after completing a B.Comm.(Hons) at the University of Manitoba.Unbeknownst to him and his bandmates, they were operating as a worker co-op.Later, Eric co-founded a multistakeholder community bike shop, Coop Vélo-Cité.He then supervised the complete renovation of the bike shop, with Sun Certified Builders Cooperative, that transformed the shop into an ultra energy efficient space.Eric loves design: thinking, art, bikes and buildings.Eric recently graduated from the Masters of Management, Co-operatives and Credit Unions program at Saint Mary's University.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".