Bibliographic record
Abstract
Less than 15% of ads are directed specifically to women and less than 5% are intended just for men. The remaining 80% are apparently targeted to everyone. This presumes very little difference in overall response between genders, which is strange, given that fundamental gender differences do exist. For example, women typically respond more positively to ads than men. Why should this be so? Is it intrinsic, is it cultural, or are there types of ads that work better with women than men, and vice versa? What leads to such differences? This paper reviews gender differences stemming from in-utero hormonal flows that shape the embryonic brain. How do such differences affect overall gender response to advertising? The findings show that advertising directed to just men or just women is more effective - yet paradoxically, it is seldom utilised, as most advertising appears to be targeted to both genders. In addition, although there is a wide range of effective styles of advertising and of content types that are demonstrably effective, many are comparatively neglected. Thus, there are opportunities for much more creativity and variety in the way advertising messages are communicated. The paper seeks to provide some clear pointers on how to go about this.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".