You Want Me to Draw What? Body Mapping in Qualitative Research as Canadian Socio-Political Commentary
Bibliographic record
Abstract
There is a scarcity of literature written about body mapping as a method to understanding mindfulness practice, and even fewer examples of how to undertake this type of research in a tangible way. In this article, we discuss how body mapping was used as part of a qualitative study investigating working mothers' mindful practices. We present a novel approach to integrating mindfulness-based techniques with SOLOMON's body mapping method. We illustrate our experiences by 1. sharing an overview of body mapping as a method, and 2. reviewing practical issues we encountered including: a) ethical issues, b) how to approach analysis, and c) body mapping within social research. Body mapping can be a fun and expressive experience for participants of social research. It can also be a confusing and overwhelming experience for researchers and participants new to the method. Through the article, we offer some insights and assurances about how to proceed with body mapping projects, including details such as how to generate questions for body mapping sessions, and a thorough consideration of steps to consider for analysis.
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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 | 0.073 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.035 | 0.041 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".