Bibliographic record
Abstract
Historically, practical knowledge and lore about breastfeeding generally has been carried across generations by women.During the writing of this book, we were fortunate to hear many, many stories of individuals' experiences with various aspects of breastfeeding.Family, friends, colleagues, and near strangers who innocently asked about our work privileged us with their own experiences and those of their private networks.In this book, we describe how the intimate and everyday practice of breastfeeding has been shaped by political and economic interests and social pressures.While we have attempted to include stories of women's actual experiences, this book is crafted primarily from written history.We hope that this book serves as a foundation for reconsidering and exploring personal experiences and family stories, and for contextualizing the oral history of breastfeeding practices in Canada.Throughout this project, we received support from an enormous number of individuals and organizations.We have been fortunate to build on the strength of previous scholarly, professional, and lay work that has documented and preserved that history and politics of infant feeding in various forms.We are grateful to all the research assistants, colleagues, mothers, lactivists, friends and family members who have strengthened this book with their insights and creativity as well as maintained a continual wellspring of enthusiasm for this work-it has been a privilege and a pleasure.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.173 | 0.132 |
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".