Consumer Databases, Neoliberalism, and the Commercial Mediation of Identity: A Medium Theory Analysis
Bibliographic record
Abstract
This paper argues that the systemic nature of contemporary consumer surveillance undermines the most fundamental principle of free market economics: consumer sovereignty. Specifically, this paper argues that the rise of an ‘information’ or ‘knowledge’ society in conjunction with neoliberal capitalism has entrenched routine forms of surveillance within commercial strategies by employing networked databases as a primary medium for the articulation of consumer sovereignty (choice/demand). The communicative relationship between consumers and producers within the market involves effectively ‘listening’ (and then responding) to consumer needs and wants in a timely manner. Surveillance is therefore not only necessary for the operation of globalized consumer capitalism, it is also the primary means by which consumer communicate their sovereignty within the marketplace. By turning to the work of Harold Innis and the intellectual tradition known as medium theory, this paper will theorize how in linking the actions of individual consumers to the decision-making capacities of trans-national corporations (TNC), the prevalence of consumer databases violates the fundamental neutrality of the market, and thus sovereignty, of individual consumers. In sum, by treating the database as a distinct communication medium, this paper will highlight how the commercial mediation of identity under neoliberalism can conceal the potential for the uneven geographic development, the marginalization of ‘less valuable’ consumer segments, and the exploitation of individual vulnerabilities through behavior and profile modeling.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".