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Record W2964034298 · doi:10.3389/fpsyg.2019.01619

Social Exclusion Shifts Personal Network Scope

2019· article· en· W2964034298 on OpenAlexaff
Joseph Bayer, David Hauser, Kinari M. Shah, Matthew Brook O’Donnell, Emily B. Falk

Bibliographic record

VenueFrontiers in Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyInterpersonal tiesSocial psychologyScope (computer science)Social exclusionSocial network (sociolinguistics)Inclusion (mineral)Task (project management)Social relationSalientInternet privacySocial mediaWorld Wide Web

Abstract

fetched live from OpenAlex

Social exclusion has the potential to alter subsequent social interactions with the members of personal networks, especially given their online availability in contemporary life. Nonetheless, there is minimal research examining how social challenges such as exclusion alter ensuing interactions with personal ties. Here, we tested whether being excluded during a social interaction changed which relationships are most salient in an ostensibly unrelated, online news sharing task. Across three operationalizations of tie strength, exclusion (vs. inclusion) increased sharing to close friends, but (unexpectedly) decreased sharing to close family members. The findings provide preliminary evidence that negative encounters may shift attention toward certain types of network ties and away from others. Future work is needed to examine how social experiences influence personal network scope - i.e., who comes to mind - in the background of daily life.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.329
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2019
Admission routes1
Has abstractyes

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