INTEREST GROUP SESSION—LONELINESS AND SOCIAL ISOLATION: THE LANGUAGE(S) OF LONELINESS
Bibliographic record
Abstract
Abstract We offer a novel perspective on the burgeoning literature focused on loneliness later life by examining the language(s) used to describe, define and depict loneliness. We have an extensive body of work describing the prevalence of , ‘vulnerability factors’ and consequences of loneliness in later life. These activities start with pre-defined concepts of what loneliness is and often use scales and questions which may/may not use the term loneliness. How well does the contemporary language of loneliness used in research, policy, practice and the media really capture the depth and complexity of what people are experiencing? Do the terms and words use in our measurement scales and quantitative research resonate with this vocabulary? In qualitative research interviews how do older adults talk (or avoid talking) about loneliness? How does the media talk about loneliness and what images does this convey about later life? We will address these three issues in our seminar. Using data from qualitative interviews undertaken as part of a mixed methods study of temporal variations in loneliness, Thomas uncovers the strategies participants used to talk or avoid talking about loneliness. Victor uses qualitative data from 12,000 adults aged 60+ collected as part of the BBC loneliness experiment to examine the terms used to describe loneliness and to identify both the opposite of loneliness and the positive aspects of loneliness. Sullivan exposes how loneliness is constructed in print and digital media over a 10-year period in the UK and Canada and its role in framing the loneliness problem.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.016 |
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".