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Record W3047512788 · doi:10.5210/spir.v2019i0.10940

GEOLOCATION: LOCATING TRUST IN DIGITAL PLATFORMS AND ECONOMIES

2019· article· en· W3047512788 on OpenAlex
Peta Mitchell, Agnieszka Leszczynski, Matthew Zook, Joe Blankenship, Caitlin McGrane, Larissa Hjorth, Julian Thomas, Rowan Wilken

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAoIR Selected Papers of Internet Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsWestern University
Fundersnot available
KeywordsGeolocationGlobal Positioning SystemSociotechnical systemComputer scienceKey (lock)GeotaggingService (business)Digital economyBusinessWorld Wide WebKnowledge managementComputer securityMarketingTelecommunications

Abstract

fetched live from OpenAlex

Digital location—or geolocation—is a fixture of digital platforms and economies, figuring as an organizational logic for content and user experience (e.g., map-based interfaces), native technical affordance (e.g., locational functionalities of GPS-enabled smartphones), and core enabling agent behind the rise of ‘disruptive’ platform enterprises (e.g., Uber, Deliveroo, and Waze all rely on geolocation for service delivery). Despite its inextricability from contemporary media, data productions, and digital practices, geolocation’s role in fostering mis/trust in digital systems has to date been unaddressed. The papers in this panel identify and theorize the ways in which geolocation functions as simultaneously a key ‘technology of trust’ (sociotechnical agent through which mis/trust is bred and/or secured in digital ecosystems), and a social relation of trustworthiness in digital platforms and economies, exploring the role geolocation plays form the perspectives of both securing trust and breeding mistrust in digital systems.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.327
Teacher spread0.296 · 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