DAOs & Co-ops: How to learn from the past to create viable economic communities for the future
Bibliographic record
Abstract
This research explores how decentralized autonomous organizations (DAOs) and Cooperative (Co-op) models can learn from each other to impact the future of community and opportunity. The findings are grounded in human-centred design, systems thinking, foresight and innovation by comparing and contrasting the two models. The insights & accessible pathways uncovered will contribute to creating healthy communities and equitable opportunities as the internet evolves. Furthermore, the results may inspire and guide future community builders and member-centred creators within emergent online spaces. Both DAOs and Co-ops have similar ambitions and starting points, and each has strengths that can complement the other's weaknesses. The goal is to highlight that DAOs and Co-ops have more similarities than differences and can work together to create a more equitable society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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 teacher head, 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".