Experienced discrimination amongst European old citizens
Why this work is in the frame
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Bibliographic record
Abstract
This study analyses the experienced age discrimination of old European citizens and the factors related to this discrimination. Differences in experienced discrimination between old citizens of different European countries are explored. Data from the 2008 ESS survey are used. Old age is defined as being 62 years or older. The survey data come from 28 European countries and 14,364 old-age citizens. Their average age is 72 years. Factor analysis is used to construct the core variable 'experienced discrimination'. The influence of the independent variables on experienced discrimination is analysed using linear regression analysis. About one-quarter of old European citizens sometimes or frequently experience discrimination because of their age. Gender, education, income and belonging to a minority are related to experienced age discrimination. Satisfaction with life and subjective health are strongly associated with experienced age discrimination, as is trust in other people and the seriousness of age discrimination in the country. Large, significant differences in experienced discrimination due to old age exist between European countries. A north-west versus south-east European gradient is found in experienced discrimination due to old age. The socio-cultural context is important in explaining experienced age discrimination in old European citizens. Old-age discrimination is experienced less frequently in countries with social security arrangements. Further research is needed to understand the variation in (old) age discrimination between European countries. Measures recommended include increasing public awareness about the value of ageing for communities and changing public attitudes towards the old in a positive way.
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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 | 0.002 | 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