Doing Dis/ordered Mappings: A New Method for Analysing Embodied and Relational Research
Bibliographic record
Abstract
Qualitative research often involves the collection of data from multiple sources, inclusive of the multisensorial, embodied and relational. These differing data sources, that are not language based, pose difficulties for analysis. Often this multimodal data is collected alongside interviews, field notesand 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, I will present a new method for analysing multisensorial, relational and embodied data, through the introduction of a tactile mapping technique. The doing of dis/ordered mappings, 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. This complex mapping suggests a need for a more holistic exploration of qualitative methodsbeyond language and visual communication. Through centralising embodiment, not only as an analytical method but also as something that informs methodologies and methods, these doings of mappings offer something new to qualitative inquiry. To evidence how the doing of dis/ordered mappings can be employed in case study research, two multi-sited case studies in Canada—the Canadian War Museum in Ottawa; and the Canadian Museum for Human Rights in Winnipeg will be discussed. This presentation illustrates how doing dis/ordered mappings as a method has the ability to produce knowledge of/ in embodied and multisensorial research in radically new ways.
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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 | 0.119 | 0.107 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".