Right this way: Exploring the use of mobile maps in <scp>Street‐Level</scp> wayfinding
Bibliographic record
Abstract
Abstract The poster explores practices of street‐level wayfinding mediated through the mobile digital map. To study everyday wayfinding, I approached people in the streets of Toronto, New York, Amsterdam and London, and asked for directions, requesting they draw their instructions. In total, I collected 220 drawings and corresponding observational fieldnotes, along with 20 supplemental interviews. Following collection, I used visual grounded theory (Konecki, Revija Za Socilologiju, 2011, 41, 131–160) and situational analysis (Clarke, Situational analysis: Grounded theory after the postmodern turn, 2005) to analyze the data, concentrating on encounters in which informants used a mobile mapping platform. The poster showcases a selection of the visual dataset and presents a theoretical framework for assessing the everyday applications of mobile mapping platforms such as Google Maps in relation to presumptions of reliability, seamlessness, and claims to space.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".