Bibliographic record
Abstract
The cultural and media studies perspectives on the technology of electronic consumer profiling. In this book Greg Elmer brings the perspectives of cultural and media studies to the subject of consumer profiling and feedback technology in the digital economy. He examines the multiplicity of processes that monitor consumers and automatically collect, store, and cross-reference personal information. When we buy a book at Amazon.com or a kayak from L.L. Bean, our transactions are recorded, stored, and deployed to forecast our future behavior—thus we may receive solicitations to buy another book by the same author or the latest in kayaking gear. Elmer charts this process, explaining the technologies that make it possible and examining the social and political implications. Elmer begins by establishing a theoretical framework for his discussion, proposing a "diagrammatic approach" that draws on but questions Foucault's theory of surveillance. In the second part of the book, he presents the historical background of the technology of consumer profiling, including such pre-electronic tools as the census and the warranty card, and describes the software and technology in use today for demographic mapping. In the third part, he looks at two case studies—a marketing event sponsored by Molson that was held in the Canadian Arctic (contrasting the attendees and the indigenous inhabitants) and the use of "cookies" to collect personal information on the World Wide Web, which (along with other similar technologies) automate the process of information collection and cross-referencing. Elmer concludes by considering the politics of profiling, arguing that we must begin to question our everyday electronic routines.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.111 | 0.078 |
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".