OLD AND LONELY: THE LONELINESS NARRATIVE, MORAL REGULATION AND THE MEDIA
Bibliographic record
Abstract
Media campaigns in the United Kingdom portray loneliness as a major social and public health issue. The aim of this study was to explore loneliness representations in the media in England and their possible relationship with key policy initiatives for an ageing population. Using a purposive sampling technique, we adopted a qualitative content analysis of print and digital media targeted at loneliness and older people over a 10 year period. Our findings suggest that skilled marketing within the media has depicted loneliness as an expected consequence of older age, a high burden of ‘disease’ for the health care sector and easily amenable to social intervention. This highly stigmatizing discourse also continues to reinforce the burden of an ageing population. We conclude that there is a need for a critical analysis of loneliness from the perspective of social and cultural constructions of ageing and the positioning of older people in society.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".