MétaCan
Menu
Back to cohort
Record W4392911664 · doi:10.15195/v11.a9

Bridging the Digital Divide Narrows the Participation Gap: Evidence from a Quasi-Natural Experiment

2024· article· en· W4392911664 on OpenAlexfundno aff
Vincenz Frey, Delia Baldassarri, Francesco C. Billari

Bibliographic record

VenueSociological Science · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
FundersUniversità BocconiEuropean CommissionRijksuniversiteit GroningenYork University
KeywordsDigital divideDisadvantagedNatural experimentThe InternetInternet accessBridging (networking)Citizen journalismCivic engagementTurnoutPoliticsPopulationInequalityPolitical scienceDemographic economicsSociologyEconomic growthEconomicsDemography

Abstract

fetched live from OpenAlex

Socio-economic inequality in access to the internet has decreased in affluent societies. We investigate how gaining access to the internet affected the civic and political participation of relatively disadvantaged late adopters by studying a quasi-natural experiment related to the American National Election Studies. In 2012, when about 80% of the U.S. population was already connected to the internet, the ANES face-to-face study was for the first time supplemented with a sample of online respondents. Our design exploits the fact that the firm (KnowledgePanel) that conducted the web survey and provided the prerecruited respondents had equipped offline sample households with free laptop computers and internet access. The findings show that gaining internet access promotes late adopters' civic participation and turnout, whereas there is no evidence for effects on the likelihood of political activism. These findings indicate that the closing of the digital divide alleviated participatory inequality.

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)0.020
metaresearch head score (Gemma)0.021
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.142
GPT teacher head0.425
Teacher spread0.283 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueSociological ScienceSame topicSocial Media and PoliticsFrench-language works237,207