9781003251569_10.4324_9781003251569-1.pdf
Bibliographic record
Abstract
Location Awareness in the Age of Google Maps explores the mundane act of navigating cities in the age of digital mapping infrastructures. Noone follows the frictions routing through Google Maps’ categorising and classifying of spatial information. Complicating the assumption that digital maps distort a sense of direction, Noone argues that Google Maps’ location awareness does more than just organise and orient a representation of space—it also organises and orients imaginaries of publicness, selfsufficiency, legibility, and error. At the same time, Location Awareness in the Age of Google Maps helps to animate the ordinary ways people are challenging and refusing Google Maps’ vision of the world. Drawing on an arts-based field study spanning the streets of London, New York, London, Toronto, and Amsterdam, Noone’s encounters of ""asking for directions"" open up lines of inquiry and spatial scores that cut through Google‘s universal mapping project. Location Awareness in the Age of Google Maps will be essential reading for information studies and media studies scholars and students with an interest in embodied information practices, critical information studies, and critical data studies. The book will also appeal to an urban studies audience engaged in work on the digital city and the datafication of urban environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.855 | 0.829 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".