Bibliographic record
Abstract
An AI approach for DE-coding subjective qualitative data into a quantitative system of information network Geo-Emotions Cartography is a participatory mapping initiative that uses geospatial tools with AI capability to collect and spatialise the intangible activities of humans (traces-emotions-feedback). The collected data represents a layer of user-lived experiences missing in urban design processes and is necessary to improve the development of human-centred design solutions. This paper presents the pedagogical approach used in a human-centred design course—an entry-level undergraduate class in the Interior Architecture and Design school. The course activities are inspired by the psychogeography concept described by Guy-Ernest Debord (1956) in Théorie de la Dérive. While la dérive [the drift] involves a low-tech, playful-constructive behaviour by participants, with an awareness of psycho-geographical effects, the Geo-Emotions capture takes a novel approach of meshing objective, high-tech big-data with subjective, low-tech, ephemeral, loosely human data that is prone to individual bias, judgment, and opinion, i.e., personal-cultural-religious. The proposal supports both city planning processes and the design of related systems—transportation, communication, and green infrastructure—with a qualitative human behavioural information system network. This pedagogical trial mirrors the systemic methods by weaving together tensions and contradictions, i.e., the result of human emotions, with science to cause the hidden attributes with their inherent relationships to emerge as complex systems for new research in human geography as subtopics of urban design. It further explores the gamified principles of la dérive theory and instils a constructivist-cognitive mindset in the students and participants. Through this process, the development of the systems of thoughts and understanding is driven by the student’s engagement with the community, and the individual’s efforts to understand one’s relationship to the environment results in cognitive development that creates a heightened collective learning experience shared back into the classroom. The students involved in this process have no prior experience with geospatial tools or analysis.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".