Sensory Data Dialogues: A Somaesthetic Exploration of Bordeaux through Five Senses
Bibliographic record
Abstract
The design of interactive systems and digital artefacts often makes use of digital or analog sensory data as a way to “capture” human senses and sensory experiences. Yet, designing for and with sensory data is complex because of our unique, embodied ways of making sense of our somatosensory experiences. Sensory data does not have one prescribed meaning for everyone. We propose a one-day Studio at TEI to start a dialogue about work with sensory data and its representation of human sensory experience. Specifically, we propose a guided walk and series of sensory explorations in Bordeaux to contemplate the interplay between first-person somatosensory experiences and streams of site-specific data from various sensors. By walking and noticing together, this Studio invites participants to engage in a process of creative reflection on their felt experiences, their connection to their surroundings, and their stance within or outside the design community.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".