Embodied Ways of Storying the Self: A Systematic Review of Body-Mapping
Bibliographic record
Abstract
The first recorded instance of whole-body-mapping for research purposes is a comparison of women's identity and the concept of the reproductive system in rural Jamaica and the UK. It was later developed in a structured workshop process in South Africa to give voice to the experiences of HIV positive individuals, decrease stigma, and advocate for provision of anti-retroviral medication. Whole-body mapping involves tracing around a person's body to create a life-sized outline, which is filled in during a creative and reflective process, producing an image representing multiple aspects of their embodied experience. Body-mapping holds promise as a qualitative, participatory research method to produce and disseminate knowledge. However, it is unclear how it is being used, by whom, and in what context. This article presents the findings of a systematic review of body-mapping in the published literature. The review identifies various implementations of body-mapping in research, therapeutic, and educational contexts. The degree of emphasis on social justice, knowledge translation, research, and therapeutic benefit varies a great deal, as does the intent and use of body-mapping. While body-mapping holds promise, more empirical investigation would be valuable in determining its characteristics in research, clinical, educative and political spheres. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1602225
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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 | 0.033 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".