Mapping the Informing Relationship: Pregnant Women’s Representations of Midwives as Information Sources
Bibliographic record
Abstract
Caring relationships are recognized as important resources for information seekers. I consider how nine pregnant women map their relationships with their midwives as they evaluate them as information sources. Data come from interviews. Women described the relationship as a trajectory, beginning with the “idea” of a midwife. As women get to know their midwife, they are able to draw on a set of resources, including the relationship itself, as informative. These resources are not static but are re-negotiated on an ongoing basis. A single encounter therefore maps both to the trajectory of the relationship and to a broader discursive community.Les relations bienveillantes sont reconnues comme ressources importantes pour ceux qui cherchent de l'information. En analysant les données de neuf entrevues, je tenterai de déterminer comment neuf femmes enceintes se représentent leur relation avec leur sage-femme vue comme source d'information. Les femmes décrivent leur relation comme une trajectoire qui s'appuie sur « l'idée » qu'elles se font d'une sage-femme. Au fur et à mesure que la relation avec leur sage-femme évolue, les femmes peuvent se prévaloir d'un ensemble de ressources informatives, y compris la relation elle-même. Ces ressources ne se veulent pas statiques, mais bien renégociées de façon continue. Une rencontre unique représente alors la trajectoire d'une relation ainsi que d'une communauté discursive.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".