Designing for Interactions with Automated Vehicles: Ethnography at the Boundary of Quantitative‐Data‐Driven Disciplines
Bibliographic record
Abstract
This case study presents ethnographic work in the midst of two fields of technological innovation: automated vehicles (AV) and virtual reality (VR). It showcases the work of three MSc. Techno‐Anthropology students and their collaboration with the EU H2020 project ‘interACT', sharing the goal to develop external human‐machine interfaces (e‐HMI) for AVs to cooperate with human road users in urban traffic in the future. The authors reflect on their collaboration with human factor researchers, data scientists, engineers, experimental researchers, VR‐developers and HMI‐designers, and on experienced challenges between the paradigms of qualitative and quantitative research. Despite the immense value of ethnography and other disciplines to collectively create holistic representations of reality, this case study reveals several tensions and struggles to align multi‐disciplinary worldviews. Results show the value of including ethnographers: 1) in the design and piloting of a digital observation app for the creation of large datasets; 2) in the analysis of large amounts of data; 3) in finding the potential of and designing e‐HMI concepts; 4) in the representation of real‐world context and complexity in VR; 5) in the evaluation of e‐HMI prototypes in VR; and finally 6) in critically reflecting on the construction of evidence from multiple disciplines, including ethnography itself.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".