Towards a Values-Based Data Governance Theory in the Social Economy in Ontario
Bibliographic record
Abstract
This thesis develops a Values-Based Data Governance (VBDG) theory by understanding the political, cultural, ideological, and historical contexts for governance of data in social economy organizations (SEOs) with a focus on Ontario, and more broadly Canada. The social economy has a different set of values which are more equity and human rights oriented compared to the public and private sectors. The primary issue with the current absence of a data governance theory is that harms to equity-seeking groups, such as breaches of the right to privacy, gender and racial inequities, and exploitation of labour, can be exacerbated by SEOs unreflexively implementing data-intensive technologies. One of the findings of this thesis is that SEOs are adopting technologies without having a coherent theory of data governance. Countering these problematic trends, and changing the trajectory of SEO adoption of technology toward a more preventive rather than reactive process requires a VBDG theory. The main research questions guiding this study are the following: What are the theoretical gaps in understanding data governance for SEOs, from a Canadian and Ontario context? Can the gaps identified be addressed by a new theory of Values-Based Data Governance (VBDG)? A VBDG theory is presented as a solution to values-based dilemmas brought on by unthoughtfully adopting data-intensive technologies, including inheriting private sector and public sector data governance theories (and practices) that exacerbate existing inequities. Deploying a critical theory of technology approach with a theory of data and algorithms as texts inspired by institutional ethnography, the thesis develops a VBDG theory by specifically examining insights generated through grey and academic literature reviews, policy and political economy analysis, and the use of illustrative case studies. The illustrative case studies highlight two significant underserved populations, people with disabilities in Ontario and immigrants to Canada, as examples to illustrate the issues brought on by an absence of or limited data governance strategies in SEOs. The thesis contributes to social economy literature by developing a VBDG theory for SEOs that provides a basis for the adoption of data-intensive technologies that mitigate socio-economic inequities for equity-seeking groups. The developed theory includes the following elements that must be taken into account when implementing a values-based approach to data governance for SEOs: (1) national culture as the primary context for data governance; (2) political economy as an additional context for data governance; (3) organizational culture as an essential component of data governance; (4) organizational incentive systems that mediate the implementation of data governance; and (5) verification and validation as required for ensuring that the principles of data governance are implemented in practice.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".