Manipulate to empower: Hyper-relevance and the contradictions of marketing in the age of surveillance capitalism
Bibliographic record
Abstract
In this article, we explore how digital marketers think about marketing in the age of Big Data surveillance, automatic computational analyses, and algorithmic shaping of choice contexts. Our starting point is a contradiction at the heart of digital marketing namely that digital marketing brings about unprecedented levels of consumer empowerment and autonomy and total control over and manipulation of consumer decision-making. We argue that this contradiction of digital marketing is resolved via the notion of relevance, which represents what Fredric Jameson calls a symbolic act. The notion of the symbolic act lets us see the centering of relevance as a creative act of digital marketers who undertake to symbolically resolve a contradiction that cannot otherwise be resolved. Specifically, we suggest that relevance allows marketers to believe that in the age of surveillance capitalism, the manipulation of choice contexts and decision-making is the same as consumer empowerment. Put differently, relevance is the moment when marketing manipulation disappears and all that is left is the empowered consumer. To create relevant manipulations that are experienced as empowering by the consumer requires always-on surveillance, massive analyses of consumer data and hyper-targeted responses, in short, a persistent marketing presence. The vision of digital marketing is therefore a fascinating one: marketing disappears at precisely the moment when it extends throughout the life without limit.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.071 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".