Place of alcohol in the ‘wellness toolkits’ of midlife women in different social classes: A qualitative study in South Australia
Bibliographic record
Abstract
In this article, we explore how women in different social classes had differential access to resources and services to enhance their 'wellness'-resulting in classed roles in alcohol consumption. We analyse data from a qualitative study on alcohol by midlife women in South Australia and employ the analogy of a 'toolkit' in order to understand the structural patterning of 'wellness tools'. Bourdieu's relational model of class guides our exploration of women's inequitable opportunities for wellness. Higher social class women had 'choices' facilitated by bulging wellness toolkits, such as yoga, exercise and healthy eating regimens-alcohol consumption was not essential to promoting 'wellness' and did not have an important place in their toolkits. Middle-class women had less well-stocked toolkits and consumed alcohol in a 'compensation approach' with other wellness tools. Alcohol consumption received positive recognition and was a legitimised form of enjoyment, fun and socialising, which needed counterbalancing with healthy activities. Working-class women had sparse toolkits-other than alcohol-which was a tool for dealing with life's difficulties. Their focus was less on 'promoting wellness' and more on 'managing challenging circumstances'. Our social class-based analysis is nestled within the sociology of consumption and sociological critiques of the wellness industry.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".