“Shopping Surveillance”: A Study of Data Harvesting, Online Behavioral Advertisement (OBA), and Regulatory Responses in the Commodification of Consumer Data
Bibliographic record
Abstract
You look at a pair of shoes for less than a minute on the website of an online retailer. Immediately afterward, the exact same pair of shoes pops up on your social media feed, with a 15% discount . This seemingly magical convenience is the end product of a pervasive and largely invisible system that now forms the backbone of the digital marketplace. This thesis examines the regulatory architecture governing this system, arguing that existing legal frameworks are structurally inadequate to address the risks associated with algorithmic profiling and its dominant application: online behavioural advertising. Grounded in the theoretical frameworks of behavioural economics and surveillance capitalism, this thesis analyzes how digital platforms exploit cognitive biases and informational asymmetries to manufacture user consent. Through a comparative analysis of three leading privacy regimes: the European Union’s General Data Protection Regulation (GDPR), Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA), and California’s Consumer Privacy Act (CCPA/CPRA), this research reveals structural limitations in the regulation of algorithmic profiling, transborder data flows, and automated decision-making. The findings demonstrate that the inadequacy of existing regulatory frameworks derives largely from a fundamental mismatch between the territorial logic of national regulation and the deterritorialized nature of the global digital market, which is engineered for jurisdictional arbitrage and regulatory evasion. In response, the thesis calls for a harmonized international framework, either in the form of a Global Data Protection Accord or a Model Law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".