Governing data in a worker cooperative
Bibliographic record
Abstract
Data governance is critical for organizations looking to leverage information and communication technology for business benefits. Common approaches to data governance, however, are based on corporate control structures that are incompatible with cooperative principles and fail to consider the characteristics of cooperative organizations. \n \nThe purpose of this thesis is to develop an understanding of data governance in worker cooperatives in the Global South and provide recommendations for implementing data governance in this context. Building on a literature review and an empirical case study of the Self-employed Women’s Association, this thesis provides insights into how data is governed within worker cooperatives and the social dilemmas that emerge. Additionally, it explores how data governance can be implemented within worker cooperatives while maintaining a sense of member control in decision-making. \n \nThe results show how data governance practices are very targeted and need-based. Data is often managed in an unstandardized form and informal norms play a big role in its governance. Three social dilemmas emerge: increased online information sharing can nourish information asymmetry, trust as a governance mechanism can become problematic with complex technology involving multiple parties, and closing the data knowledge gap is challenging due to several reasons related to resources, access, and information asymmetry. \n \nThis thesis gives practical implications that will benefit cooperatives building self- governed data systems. New technologies for knowledge sharing must be implemented with consideration and such that all members can participate. Formally defining boundaries for data resources and setting ground rules for sharing, access, and use is key. Further, building successful participatory data governance in a context of low data literacy could benefit from a data privacy tutoring program. \n \nThis thesis can influence policymakers and developers of ICT by showcasing the importance of inclusivity, legislative initiatives, and policy development in balancing the power dynamics of the digital world, protecting the most vulnerable, and ensuring a fair playing field in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.045 |
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; both teacher heads agree on what is shown here.
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".