Breast-feeding, Bottle-feeding and Dr. Spock: The Shifting Context of Choice*
Bibliographic record
Abstract
Dans l'environnement d'aujourd'hui, l'allaitement naturel représente à la fois un exemple médical idéal pour l'alimentation infantile et un exemple moral idéal pour les soins maternels. Le caractère moralement chargé de ce discours rend la notion de choix dans l'alimentation infantile particulièrement problématique et lourde de difficultés. À partir d'une analyse de contenu historique d'éditions choisies de 1946 à 1998 du célèbre manuel de soins de l'enfant du Dr Spock, l'auteure de cet article explique le processus par lequel le discours du sein contre le biberon s'est transformé au cours du dernier demi-siècle et comment ce change-ment a influé sur le contexte du choix à l'intérieur duquel les mères doivent décider comment nourrir leur enfant. In today's environment, breast-feeding represents both a medical gold standard for infant feeding and a moral gold standard for mothering. The morally charged character of this discourse makes the notion of choice in infant feeding particularly problematic and fraught with difficulty. From an historical content analysis of selected editions from 1946 to 1998 of Dr. Spock's famous child-care manual, this paper explicates the process through which the breast versus bottle discourse has shifted over the last half-century, and how these shifts have shaped the context of choice within which mothers must make their infant-feeding decisions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".