MétaCan
Menu
Back to cohort
Record W4285260894 · doi:10.1177/16094069221105382

Birth Mapping: A Visual Arts-Based Participatory Research Method Embedded in Feminist Epistemology

2022· article· en· W4285260894 on OpenAlex
Kaveri Mayra

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of British Columbia
FundersBurdett Trust for NursingMichael Smith Health Research BC
KeywordsOppressionChildbirthGender studiesVisual researchNarrativeParticipatory action researchReproductive healthSociologyVisual methodsPsychologyPregnancyVisual artsAnthropologyDemographyPolitical scienceArtPoliticsPopulation

Abstract

fetched live from OpenAlex

Reproductive and sexual health of women are sensitive areas of enquiry characterized by strong cultural oppression of women. Body mapping, an arts-based participatory research method, has proven useful in research with such sensitive topics. In this paper, I describe my experience of researching women’s experience of childbirth through birth maps, an adaptation of body mapping. Live size maps were co-created along with birthing story and body key with women in Bihar, India. Body mapping is a very cost-effective method that ensures better recall, richer narratives, reduced power-based inequalities that enables to explore reproductive, maternal & sexual health topics respectfully. The birth map and birthing story can generate awareness about how women give birth, as an attempt to improve the quality and respectfulness in care provision during labour and childbirth.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.289
metaresearch head score (Gemma)0.043
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2890.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.967
GPT teacher head0.830
Teacher spread0.137 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it