Doing Embodied Mapping/s: Becoming-With in Qualitative Inquiry
Bibliographic record
Abstract
Qualitative research often involves the collection of data from multiple sources, inclusive of the embodied and multisensorial. These differing data sources, that are not language based, pose difficulties for researchers. Often this multimodal data is collected alongside interviews, field notes and other language-based data and then translated into language. In the process of this translation, the embodied, relational, and multisensorial aspects of this data is often lost. To address this issue, we created E mbodied Mapping/s (EM) as an approach for collecting, analyzing and becoming-with non-language-based data. This doing of embodied mapping/s is not about fixing lines and encounters in order to produce a two-dimensional cartography, plan or model; on the contrary it is about exploring differing embodiments and material relations among people and things to create a new inquiry in embodied and multisensorial research and methodologies. Embodied mapping/s suggests a need for a more holistic exploration of qualitative methodologies beyond language and visual communication. Through centralising embodiment, not only as an analytical method but also as something that informs innovative methodologies and methods, these doings of embodied mapping/s offer something novel to qualitative inquiry and embodied methodologies. To evidence the doing of embodied mapping/s, two multi-sited case studies in Canada will be explored—the Canadian War Museum in Ottawa; and the Canadian Museum for Human Rights in Winnipeg, to advance methodological insights in relation to multimodal and multisensorial research.
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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 | 0.223 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.018 | 0.060 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".