Cigarette Advertising to Counter New Years Resolutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One process through which tobacco advertising may work is by reducing rates of quitting. Theories of addiction support the notion that relapse can be prompted by environmental cues. Further, because withdrawal symptoms occur over a predictable time frame, and because the most popular time to quit smoking is the beginning of the year, as a New Year's resolution, tobacco companies can make use of advertising to remind quitters of their need to smoke. Study 1 examined advertising in 10 popular magazines. It found a higher number of ads in January and February than the rest of the year after 1984. Study 2 examined cigarette advertising on the back cover of 10 other popular magazines. This study also found a higher rate of cigarette advertisements in January and February than for the rest of the year. The results suggest that cigarette marketers may be attempting to preempt quitting by cuing smoking behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it