The age of persuasion: how marketing ate our culture
Bibliographic record
Abstract
Consider the culture of the twenty-first century: Each morning, you hear a half-dozen ads on the radio before your feet touch the floor. By the end of the day, hundreds--perhaps thousands--of marketing messages have targeted you. And yet little is understood about how marketing affects our lives and society. Enter Terry O'Reilly and Mike Tennant, the ad men behind The Age of Persuasion, the popular radio show broadcast on the Canadian Broadcasting Corporation and Sirius Radio. They have made it their mission to share the back-room story of modern marketing, entertaining asides and all: Think of advertisers as millions of ants in a colony, each working hard and each with its own objective. Except that in this colony, every single ant is competing against the others. That's the ad business. Almost every ad you see, hear, and otherwise experience is competing for a piece of your imagination. And like any cross-section of humanity, the vast, worldwide advertising community is diverse: composed of geniuses and idiots, saints and buffoons, and everything in between. From the early players to the Mad Men of the 1960s and beyond, The Age of Persuasion provides an entertaining--and eye-opening--look at a world driven by marketing.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.036 | 0.025 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".