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Record W2508861790 · doi:10.1371/journal.pone.0162130

The Social Costs of Ubiquitous Information: Consuming Information on Mobile Phones Is Associated with Lower Trust

2016· article· en· W2508861790 on OpenAlexaff
Kostadin Kushlev, Jason Proulx

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOpinion Dynamics and Social Influence
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInternet privacyComputer scienceBusinessUbiquitous computingMobile deviceWorld Wide WebComputer securityHuman–computer interaction

Abstract

fetched live from OpenAlex

In an age already saturated with information, the ongoing revolution in mobile computing has expanded the realm of immediate information access far beyond our homes and offices. In addition to changing where people can access information, mobile computing has changed what information people access-from finding specific directions to a restaurant to exploring nearby businesses when on the go. Does this ability to instantly gratify our information needs anytime and anywhere have any bearing on how much we trust those around us-from neighbors to strangers? Using data from a large nationally representative survey (World Values Survey: Wave 6), we found that the more people relied on their mobile phones for information, the less they trusted strangers, neighbors and people from other religions and nationalities. In contrast, obtaining information through any other method-including TV, radio, newspapers, and even the Internet more broadly-predicted higher trust in those groups. Mobile information had no bearing on how much people trusted close others, such as their family. Although causality cannot be inferred, these findings provide an intriguing first glimpse into the possible unforeseen costs of convenient information access for the social lubricant of society-our sense of trust in one another.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.323
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.226
Teacher spread0.213 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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