Profits Over Privacy: Investigating the Under-Regulated Business Practices of the Global Data Brokerage Industry in Canada
Bibliographic record
Abstract
This dissertation contends that in pursuit of profits, Canada’s privacy regime relies on quasi-self-regulation, non-prescriptive principles, and weak limitations on the collection, use, and disclosure of personal information—all of which permits and encourages the monetization of personal information by data brokers. As the government seeks to promote innovation and economic growth, commercial interests have been placed ahead of consumer privacy. The prioritization of profits over privacy, coupled with the Canadian Government’s limited knowledge of data brokers, exacerbates the insufficient regulation of data brokers in Canada and minimizes the need for these firms to exercise their business power to advance their interests. To advance this argument, this dissertation establishes a unique theoretical framework that lays the foundation to examine and explain the monetization of Canadian personal information by data brokers. Additionally, this dissertation employs a qualitative methodological approach that combines elite interviews, House Standing Committee testimony and an analysis of lobbying data. This dissertation makes four key contributions to knowledge. First, by focusing on data brokers in Canada, this dissertation expands the literature on privacy, Big Data, and business power to an issue area and country that have received inadequate scholarly attention. With the novel digital age data broker typology and a Data Brokers in Canada List, this dissertation has provided empirical insights into an industry that is notoriously opaque. Second, it identifies three features of Canada’s privacy regime that enable traditional and digital age data brokers to monetize personal information. Third, this thesis identifies five avenues of influence through which data brokers can, but largely do not, exercise their business power. Together, these avenues were utilized to diagnose a very weak degree of regulatory capture by data brokers. Lastly, this dissertation contributes to the understanding of regulation as a tool to mitigate harms and to promote innovation and highlights how capture theory inadvertently excludes implicit forms of capture that are still hazardous.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.034 | 0.018 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".