Geospatial Mapping: Spatialising habitus in the studies of psychogeography lived experience
Bibliographic record
Abstract
A virtual experience platform as a service (PAAS) This workshop explores a hands-on systemic model-builder approach through users’ engagement and participation in developing a cross-collaborative platform celebrating users’ differences. The session engages participants in user-centred design research and urban analytics featuring enhanced integration of geoscience, machine learning and automated sensors in making efficient urban systems. Activities include presenting the App, using it to capture emotions, and visualising the results through infographics, data analytics for geospatial assessments and discussions to help understand how intangible data can support urban planning. Capture activities are deployed into the cloud and visualised on dashboards and other forms of interactive infographics. Participants are encouraged to explore all intangible emotion buttons and tangible point captures. Participants contribute to the cloud-based geo-info-hub that houses the collective effort; however, the data capture is anonymous, revealing only the location and feedback provided (no other user information is collected). A smartphone and QR code are used to download the Geo Emotions application (the App). Participants select an area of interest, indoor or outdoor, and spend 30 minutes experiencing and recording their emotions. This is a transdisciplinary, cross-cultural and transgenerational initiative that weaves similarities and contradictions to emerge novel ideas in systemic design.
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.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".