Doing Embodied Mapping/s: Becoming-With in Qualitative Inquiry
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.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.112 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it