From Here To: Everyday Wayfinding in the Age of Google Maps
Bibliographic record
Abstract
Today, asking for directions is often associated with “asking” a mobile mapping\napplication like Google Maps. Google Maps is one of the most popular applications for mobile\ndevices with over 1 billion users per month. What does everyday wayfinding look like in the age\nof digital mapping and locative media? My doctoral research is a creative and critical look at the\neveryday information seeking and sense-making practices of urban wayfinding within conditions\nof mobile mapping platforms. I approach this line of inquiry using exploratory arts-based\nresearch methods, specifically spontaneous drawing and performance. In this capacity, I walked\nthe streets of four cities, asking passers-by for directions, requesting the passerby draw out their\nrecommended route using the paper and pen I provided. I selected Toronto, New York,\nAmsterdam, and London as my urban contexts based on their different topographies and Englishlanguage\nproficiencies. The directions I asked for were to and from preselected sites such as\nshopping areas, transit hubs, civic squares, local parks, and public libraries. In total, I engaged in\n220 directional encounters (55 per city) resulting in 220 hand-drawn route maps, with\ncorresponding fieldnotes and selected interviews. I analyzed my data based on Visual Grounded\nTheory, an iterative analytical process that works across the different data types and connects to\nthe data’s social modalities.\nThe mobile digital map was often used to “double-check” spoken directions, to “show”\nme the way, or to determine the “best route.” Wayfinding through the city was also made legible\nthrough the city’s physical forms and infrastructures such as the tramlines and roadways, as well\nas qualitative descriptions and features of different locations. In addition, these encounters\nrevealed how embodied information practices are presented and represented when describing\nhow to get from A to B. Findings show the complexity of everyday wayfinding, negotiated\nthrough the tacit and material forms of technological interventions, urban configurations, and\ninformation affects. My research provides methodological insight into arts-based methods in\ninformation studies, situating the drawing event at the thresholds of information spaces and civic\nsites. My analysis and findings result in an empirically-informed theoretical framework by which\nto critically approach the information practice of urban wayfinding. This framework can be\nfurther applied to investigate the spatial and temporal values Google Map’s promotes in relation\nto the everyday information practices of street-level navigation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 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".