Body Maps as Ecological, Affective, Relational and Decolonizing Method
Bibliographic record
Abstract
Building upon previous research (Fidyk 2019a, 2019b) aimed to support the mental health of vulnerable youth, this presentation focuses on body maps as an ecological, affective, relational and decolonizing method for data creation, collection and expression/presentation. Body maps, broadly defined, are life-size body images, while body mapping is the process of creating body maps using collage, photography, painting or other arts-integrated techniques to visually symbolize aspects of people’s lives, bodies and worlds. Rooted in research regarding women’s reproductive health and body biology in Jamaica (MacCormack, 1985), body maps became widely used as a method to study HIV/AIDS self-care needs. It has been used in community-based work in Zimbabwe (Cornwall, 1990) and South Africa (MacGregor, 2009), and in Brazil, Colombia, Canada and Mexico (Devine, 2008; Gastaldo et al., 2012; Gastaldo et al., 2018; Wienard, 2006). The use of body maps also enables participants to engage with sites of injury, even trauma, yet in a safe, playful way (Crawford, 2010; Haiman, 2013; van der Kolk, 2014; Orchard, 2017). Indigenist (Wilson, 2008), feminist, anti-colonial, anti-race and decolonizing theories value its trans nature because participants can “speak” through counter-hegemonic discourses. For example, participants rejected the naming of girl and boy, choosing “something in-between” but not opting for terms such as transgender. Of significance, body maps support poetic approaches to research that respect imagination, sensation and body awareness. The art of body mapping serves those who seek witnessing, testimony and social justice. Moving beyond a historical and contemporary analysis of the method, its strengths and limitations are discussed, particularly via interdisciplinary research and pedagogical praxis.
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.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".