The Everyday Information Experience of Street-Level Wayfinding: A Critical Look
Bibliographic record
Abstract
In the following paper I look at the information practice of wayfinding - the means by which people orient in and navigate through spaces. In contemporary information conditions of networked mobility, wayfinding is often associated with ‘asking’ Google Maps to locate where something is and how to get there. Google Maps is the most popular application for mobile devices with over 1 billion people putting it to work every month. Despite this frequency, there is little information available on how Google Maps is used. As technology writer Andrew J. Hawkins proclaims (2017), “we just need the directions, the right subway route, or the name of that good sushi place.” What is happening in these moments when one needs directions? And more specifically, to paraphrase Sarah Sharma (2012), whose routes become reified by Google Maps? I argue that the imaginary of the Google Maps ‘user’ is more than simply an archetype but an orientation within a spatialization of information that are made evident in acts of everyday wayfinding. The paper’s focus is to reflect on the intersections and the divergences between the Google Maps rhetoric and the types of street-level observed during the research. Dans l'article suivant, j'examine la pratique informationnelle de l'orientation - les moyens par lesquels les gens s'orientent et naviguent dans les espaces. Dans le contexte informationnel contemporain caractérisé par la mobilité en réseau, l’orientation est souvent associée au fait de «demander» à Google Maps de localiser où se trouve quelque chose et comment s’y rendre. Google Maps est l'application la plus populaire pour les appareils mobiles avec plus d'un milliard de personnes qui l'utilisent chaque mois. Malgré cette fréquence, peu d'informations sont disponibles sur la manière dont Google Maps est utilisé. Comme le proclame l'écrivain technologique Andrew J. Hawkins (2017, notre trad.), « nous avons seulement besoin de directions, du bon trajet de métro, ou du nom de ce bon restaurant de sushi.» Que se passe-t-il dans ces moments où l'on a besoin de directions? Et plus précisément, pour paraphraser Sarah Sharma (2012), qui voient leurs itinéraires réifiés par Google Maps? Je soutiens que l’imaginaire de «l’utilisateur» de Google Maps est plus qu’un simple archétype, mais une orientation dans une spatialisation de l'informations qui se manifestent dans des actes quotidiens d'orientation. L’objectif de l’article est de réfléchir aux intersections et aux divergences entre la rhétorique de Google Maps et celle des passants observées au cours de la recherche.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.021 | 0.077 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".