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Record W2170082337 · doi:10.1177/00027640121957286

Does the Internet Increase, Decrease, or Supplement Social Capital?

2001· article· en· W2170082337 on OpenAlexaff
Barry Wellman, Anabel Quan‐Haase, James C. Witte, Keith N. Hampton

Bibliographic record

VenueAmerican Behavioral Scientist · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThe InternetSocial capitalVoluntary associationInterpersonal communicationPoliticsSociology of the InternetPublic relationsAffect (linguistics)Interpersonal relationshipScale (ratio)BusinessInternet researchPsychologyPolitical scienceSocial psychologySociologySocial scienceWorld Wide WebGeography

Abstract

fetched live from OpenAlex

How does the Internet affect social capital? Do the communication possibilities of the Internet increase, decrease, or supplement interpersonal contact, participation, and community commitment? This evidence comes from a 1998 survey of 39,211 visitors to the National Geographic Society Web site, one of the first large-scale Web surveys. The authors find that people's interaction online supplements their face-to-face and telephone communication without increasing or decreasing it. However, heavy Internet use is associated with increased participation in voluntary organizations and politics. Further support for this effect is the positive association between offline and online participation in voluntary organizations and politics. However, the effects of the Internet are not only positive: The heaviest users of the Internet are the least committed to online community. Taken together, this evidence suggests that the Internet is becoming normalized as it is incorporated into the routine practices of everyday 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.370
Teacher spread0.336 · 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

Citations1,691
Published2001
Admission routes1
Has abstractyes

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