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Record W3092132530 · doi:10.5210/spir.v2020i0.11293

GOING IN A DIFFERENT DIRECTION: CRITICAL ARTS-BASED APPROACHES TOGOOGLE MAPS

2020· article· en· W3092132530 on OpenAlexaffabout
Rebecca Noone

Bibliographic record

VenueAoIR Selected Papers of Internet Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAffordanceRelation (database)Point (geometry)Computer scienceSpatial analysisDigital mappingWorld Wide WebCartographyGeographyHuman–computer interactionDatabase

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.172
GPT teacher head0.396
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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