Discipline and Power in the Digital Age: The Case of the US Consumer Data Broker Industry
Bibliographic record
Abstract
While many scholars have commented on the rise of consumer finance, few have sought to critically explore a largely invisible segment of this industry: the multi-billion dollar consumer data broker industry. The industry has amassed trillions of digital consumer records, or ‘big data’, that are stockpiled, analyzed, and sold. The odious under-regulation of this industry, as well as the role it plays in consumer debt and data breaches, often flies under the radar of critical social analyses. I adopt a neo-Gramscian approach to make sense of the material, historical, and social dimensions of this phenomenon. This article asks: what are the sources and nature of this industry’s power, and how does it relate to consumer debt? The analysis suggests that these systems demonstrate how financial capital can constitute certain domains of public policy.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.029 | 0.035 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".