Ageing, Diversity and Equality : Social Justice Perspectives
Bibliographic record
Abstract
Current understandings of ageing and diversity are impoverished in three main ways. Firstly, with regards to thinking about what inequalities operate in later life there has been an excessive preoccupation with economic resources. On the other hand, less attention has been paid to cultural norms and values, other resources, wider social processes, political participation and community engagement. Secondly, in terms of thinking about the ‘who’ of inequality, this has so far been limited to a very narrow range of minority populations. Finally, when considering the ‘how’ of inequality, social gerontology’s theoretical analyses remain under-developed. The overall effect of these issues is that social gerontology remains deeply embedded in normative assumptions which serve to exclude a wide range of older people.\n\nThis book aims to challenge and provoke the above described normativity and offer an alternative approach which highlights the heterogeneity and diversity of ageing.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".