An <i>Illicit</i> Way to Elicit Public Health Responses to Obstetric Violence: Poetic Inquiry of Open-Ended Survey Data
Bibliographic record
Abstract
Sensitive areas of research require sensitive ways of inquiry. Despite the growth of research on obstetric violence, its recognition as a form of gender-based violence continues to be contested and met with resistance. As a problem stemming from gendered power imbalances, we explored obstetric violence from a critical feminist lens, situating the different parts of the exploration such as analysis, presentation and integrated knowledge translation into arts-based research. In this paper, we describe poetic inquiry as an innovative, and effective method for analyzing a large qualitative dataset of thin data on sensitive and/or controversial topics. We applied voice-centred relational analysis to 2,741 open text patient-reported narratives on obstetric violence reported in the nationwide Canadian RESPCCT survey. We present three poetry iterations: sparse IPoems, full IPoems and context poems using the Voice Centered Relational Analysis. Each type of poem and the way of reading them aims to impact the audience in distinct ways by evoking different feelings and approaches to engaging with the participants’ narratives. Drawing on principles of intersectionality theory and standpoint theory, we present a detailed description of our positionalities to give the readers an insight into how we practiced reflexivity while researching a sensitive subject. With this paper we intend to further the discourse and utility of arts-based research, particularly poetic inquiry, in analyzing and presenting open text survey responses.
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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 | 0.070 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".