Slipping Under the Radar: Advertising and the Mind
Bibliographic record
Abstract
have combined over the past 60 years to create very powerful tools of influence. These tools have proven to be capable of shaping the attitudes, values and behaviors of large numbers of people. This paper explains the power to influence in the context of recent discoveries in brain science. In addition, comments are made about adopting these techniques to promote a public health agenda. I. The Power of Media. In the early 1970s, government and business leaders in Mexico were confronted with a serious problem. The world economy was shifting to an information economy, making the ability to read and write more important than ever. At the same time, rates of adult literacy remained stubbornly low in many Mexican workplaces. After several failed initiatives, Miguel Sabido, the producer of a very popular television program tried an experiment. For a number of months in 1973 Sabido wove pro-adult literacy messages into the plot of his top-rated program. Most of the messages came out of the mouth of the favorite male lead character. In the twelve months following that experiment, registrations in adult literacy classes across Mexico increased by an astounding 800 % (Ryerson,
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".