Bibliographic record
Abstract
The ‘aging well’ discourse advances the idea of making older people responsible for their capability to stay healthy and active. In the context of an increased ageing population, which poses several challenges to countries’ government, this discourse has become dominant in Europe. We explore the way older people are visually represented on websites of organizations for older people in seven European countries (Finland, UK, the Netherlands, Spain, Italy, Poland and Romania), using an analytical approach based on visual content analysis, inspired by the dimensional model of national cultural differences from the Hofstede model (1991; 2001; 2011). We used two out of the five Hofstede dimensions: Individualism/Collectivism (IDV) and Masculinity/Femininity (MAS). The results demonstrated that in all seven countries older people are mostly visually represented as healthy/active, which reflects a dominant ‘ageing well’ discourse in Europe. The results also demonstrated that in most cases older people tend to be represented together with others, which is not consonant with the dominant ‘ageing well’ discourse in Europe. A last finding was that the visual representation of older peopleis in about half of the cases in line with these Hofstede dimensions. We discuss the implications of these findings claiming that the ‘ageing well’ discourse might lead to ‘visual ageism’. Organizations could keep this in mind while using pictures for their website or in other media and consider to use various kind of pictures, or to avoid using pictures of older people that stigmatize, marginalize or injure. They could look into the cultural situatedness and intersectional character of age relations and consider alternative strategies of both visibility and invisibility to talk with and about our ageing societies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.262 | 0.096 |
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".