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In the mix: Social integration and social media adoption

2019· article· en· W2917561171 on OpenAlexfundno aff
Hana Shepherd, Jeffrey Lane

Bibliographic record

VenueSocial Science Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
FundersNational Science Foundation of Sri LankaCanadian Institute for Advanced ResearchResearch Council, Rutgers, The State University of New JerseySpencer FoundationRussell Sage Foundation
KeywordsSocial mediaSocial integrationDominance (genetics)Community integrationSociologySocial engagementSocial competenceSocial network (sociolinguistics)Public relationsPsychologySocial changePolitical scienceComputer scienceSocial scienceWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

What is the nature of the relationship between online and offline social life? Specifically, how does participation in the traditional forms of social life of a community shape social media adoption? Using a unique, two-wave panel dataset with saturated network data from over 20,000 students in 56 New Jersey middle schools, we test how measures of integration into a community are associated with adoption and discontinuation of social media platforms over the course of a school year. Social media adoption and discontinuation are related to the extent of students' bonding integration (social connections and social support) and competitive integration (related to status and dominance hierarchies) into the social life of the school, where more integrated students are more likely to adopt more social media platforms. Social media provides a space for community members to be in the know and to be known, both of which are more valuable with greater social integration.

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)0.002
metaresearch head score (Gemma)0.012
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0070.007
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.101
GPT teacher head0.468
Teacher spread0.367 · 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

Citations84
Published2019
Admission routes1
Has abstractno

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