GOING IN A DIFFERENT DIRECTION: CRITICAL ARTS-BASED APPROACHES TOGOOGLE MAPS
Bibliographic record
Abstract
These days, wayfinding is often associated with ‘asking’ Google Maps for directions. Over one billion people per month use Google Maps and Google estimates that one-in-three mobile searches is location-related. Whose routes does Google Maps direct one towards and what types of pathways become naturalized? I approached these questions through performance- and drawing-based research. In the early 1960s, artist Stanley Brouwn stood on a street corner in Amsterdam and asked people for directions with pen and paper in hand. Over the course of the project, Brouwn amassed a collection of hand drawn maps. Now, in contemporary conditions of geomedia, I reactivated Brouwn’s work as an exploratory research project in Toronto, New York City, London, and Amsterdam. I also collected drawings and notes, spontaneously produced in situ, which I analyzed in relation to the location-awareness and real-time feedback of digital mapping technologies. The encounters produced notes and drawings that point to the information literacies used in street-level wayfinding and situate these in relation to the mobilities and spatial perceptions prioritised by Google Maps’ interface and affordances. The paper presents a theoretical framework to assess the types of calibrations at play when proprietary mapping platforms broker spatial information. The paper uses alternative research methods to explore how Google Maps is more than a guide but an orientation towards a Google spatial imaginary while at the same time complicated by the ad hoc layering of other wayfinding strategies.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.033 | 0.163 |
| Scholarly communication | 0.034 | 0.023 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".