Positioning theory and the negotiation of information needs in a clinical midwifery setting
Bibliographic record
Abstract
Abstract Studies of everyday life information seeking have begun to attend to incidental forms of information behavior, and this more inclusive understanding of information seeking within broader social practices invites a constructionist analytical paradigm. Positioning theory is a constructionist framework that has proven useful for studying the ways in which interactional practices contribute to information seeking. Positions can construct individuals or groups of people in ways that have real effects on their information seeking. This article identifies some specific types of discursive positioning and shows how participants in a clinical care setting position themselves and one another in ways that justify different forms of information seeking and giving. Examples are drawn from an ongoing study of information seeking in prenatal midwifery encounters. The data consist of audio recordings of nine prenatal midwifery visits and of 18 follow‐up interviews, one with each participating midwife and pregnant woman. The midwifery model of care is based on a relationship in which the midwife provides the pregnant woman with information and support necessary for making informed decisions about her care. Midwife–client interactions are therefore an ideal context for studying information seeking and giving in a clinical encounter.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".