Contextualising risk, constructing choice: Breastfeeding and good mothering in risk society
Bibliographic record
Abstract
The ‘whats’ and ‘hows’ of feeding babies is a key interest in the arena of public health. In recent years, this has translated into an ever-increasing emphasis on breastfeeding; namely, on trying to get more mothers to breastfeed, to breastfeed exclusively, and to breastfeed for longer. It is argued, however, that this discourse is not a benign communiqué about the relative benefits of breastfeeding, but an ideologically infused, moral discourse about what it means to be a ‘good mother’ in an advanced capitalist society. With the dual aim of (a) building upon existing cultural analyses of infant feeding, and (b) furthering our understanding of the construction of' ‘good mothering’ in risk society, this paper examines how notions of risk/benefit are taken up and used in mothers' talk about their infant feeding decisions and experiences. The findings detailed in this paper support the thesis that the authority to define and monitor ‘risk’ in parenting is increasingly the purview of medical-scientific discourse. The analysis further demonstrates how, within such a framework, mothers' risk consciousness vis-a-vis infant feeding is activated primarily as an issue of identity, of ‘good mothering’ as defined by the dominant, expert-guided, scientific-medical discourse.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 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; 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".