Sharing Like We Mean It: Working Co-operatively in the Cultural and Tech Sectors
Bibliographic record
Abstract
A hybrid research report and co-op primer, Sharing Like We Mean It: Working Co-operatively in the Cultural and Tech Sectors is based on a survey of 106 co-operatives in Canada, the UK, and the US. It offers a snapshot of the co-op landscape in creative industries, explores what co-operative work can look like in practice, and features profiles of several worker co-operatives. Our survey results confirm that the co-operative model is a promising strategy for mitigating individualized patterns of work, democratizing work relationships, and providing satisfying work in creative industries contexts. Co-ops are not a magic solution to systemic work problems. But the co-op model – in conjunction with other pro-worker policies and organizations – holds potential to democratically remake work in ways that have yet to be fully realized, or widely tested, in creative industries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".