Bibliographic record
Abstract
How marketers learned to dream of optimization and speak in the idiom of management science well before the widespread use of the Internet. Algorithms, data extraction, digital marketers monetizing "eyeballs": these all seem like such recent features of our lives. And yet, Lee McGuigan tells us in this eye-opening book, digital advertising was well underway before the widespread use of the Internet. Explaining how marketers have brandished the tools of automation and management science to exploit new profit opportunities, Selling the American People traces data-driven surveillance all the way back to the 1950s, when the computerization of the advertising business began to blend science, technology, and calculative cultures in an ideology of optimization. With that ideology came adtech, a major infrastructure of digital capitalism. To help make sense of today's attention merchants and choice architects, McGuigan explores a few key questions: How did technical experts working at the intersection of data processing and management sciences come to command the center of gravity in the advertising and media industries? How did their ambition to remake marketing through mathematical optimization shape and reflect developments in digital technology? In short, where did adtech come from, and how did data-driven marketing come to mediate the daily encounters of people, products, and public spheres? His answers show how the advertising industry's efforts to bend information technologies toward its dream of efficiency and rational management helped to make "surveillance capitalism" one of the defining experiences of public life.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.113 | 0.031 |
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".