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Record W3135754616 · doi:10.1371/journal.pone.0247996

Analysis of mobility homophily in Stockholm based on social network data

2021· article· en· W3135754616 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePLoS ONE · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsnot available
FundersTeck ResourcesHorizon 2020Horizon 2020 Framework ProgrammeKungliga Tekniska HögskolanFondazione Centro Studi EnelGoverno BrasilFord Foundation
KeywordsHomophilySocioeconomic statusProxy (statistics)Similarity (geometry)Metric (unit)Geographical distanceGeographySocial network (sociolinguistics)DemographySocial mediaComputer scienceSociologyStatisticsWorld Wide WebMathematicsEconomicsPopulationSocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We present a novel metric for measuring relative connection between parts of a city using geotagged Twitter data as a proxy for co-occurrence of city residents. We find that socioeconomic similarity is a significant predictor of this connectivity metric, which we call "linkage strength": neighborhoods that are similar to one another in terms of residents' median income, education level, and (to a lesser extent) immigration history are more strongly connected in terms of the of people who spend time there, indicating some level of homophily in the way that individuals choose to move throughout a city's districts.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.137
GPT teacher head0.331
Teacher spread0.194 · 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