ROLE OF CO-OPERATIVES IN FACILITATING THE IMPLEMENTATION OF THE SUSTAINABLE DEVELOPMENT GOALS: AN EXPERIENCE FROM NOVA SCOTIA, CANADA
Bibliographic record
Abstract
Attainment of the Sustainable Development Goals (SDGs) will require concerted effort throughout societies everywhere. Although co-operatives, as community-embedded local organizations, have been promoted in advancing SDGs, to what extent co-operatives are furthering the SDGs is understudied. Here I analyze the extent to which 179 co-operatives in Nova Scotia, Canada are facilitating the SDGs in a two-step process. First, the SDGs were re-expressed for a Canadian, localized context. Second, the resulting framework was then used to analyze the contents of co-op mission statement. Patterns of mission statement alignment with the SDGs were further analyzed against co-op characteristics. Results indicate that the purposes of NS-based co-ops do align with many SDGs. However, the alignment is highly variable across different co-op sectors and environmental-related SDGs remain largely unsupported. Methodological and empirical practices are suggested to further holistically assess and enhance the impact of co-operatives on advancing the SDGs, especially from an environmental perspective.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".