The invisible majority? Older models in UK television advertising
Bibliographic record
Abstract
AbstractThis paper presents the results of the first content analysis of the inclusion and portrayal of older models (50+) in UK prime-time television advertising. Findings suggest that older models are not portrayed as stereotypically old. However, as a whole, and in particular women, they are still under-represented in major roles, and there is an apparent reluctance by marketers in some product categories to portray older models at all. Additional informationNotes on contributorsPeter SimcockPeter Simcock is a Senior Lecturer in Marketing in the School of Management, Liverpool John Moores University. After completing his undergraduate degree in 1977, he worked in industry and returned to education in 1984, gaining an MSc in Marketing Management. He has published and presented papers on the older consumer in the United Kingdom, Europe and the USA.SudburyDr. LynnLynn Sudbury entered higher education as a mature student after 13 years working in industry. After gaining a first class honours degree in Business Administration, she became a lecturer in the School of Management. Her PhD is on 50+ consumers, and she has published articles in the fields of consumer behaviour, marketing and gerontology, and has presented papers in the United Kingdom, Europe and the USA.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".