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Weak Ties (Strength of)

2017· other· en· W4238297977 on OpenAlexaff
David B. Tindall, Todd E. Malinick

Bibliographic record

VenueThe Blackwell Encyclopedia of Sociology · 2017
Typeother
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInterpersonal tiesClosenessArgument (complex analysis)Variety (cybernetics)ConceptualizationCollective actionSociologyStrong tiesAction (physics)Social psychologyEpistemologyPsychologySocial sciencePolitical scienceComputer scienceLawMathematics

Abstract

fetched live from OpenAlex

Abstract Weak ties are relationships between individuals marked by relatively low levels of intensity and emotional closeness. By contrast, strong ties are relationships that involve high levels of intensity and emotional closeness. The importance of weak ties to a variety of sociological phenomena has been most influentially articulated by Mark Granovetter in the most cited article in sociology, “The Strength of Weak Ties” (SWT). Granovetter argues that most people intuitively expect strong ties to generally be more important than weak ties, because those to whom we are closely tied are more motivated to help us, and are also more likely to be stronger sources of social influence and social support. However, basing his argument on principles of social psychology, Granovetter argues that weak ties are – paradoxically – more important for a variety of phenomena, from helping people obtain a job, to the diffusion of ideas and innovations, to facilitating collective action. This entry describes the key theoretical underpinnings of Granovetter's SWT argument, and its application to various topics including job searches, and collective action and social movements. Also discussed are contextual issues, conceptualization and measurement, virtual networks, and related problems and future research.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.073
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.007
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.293
Teacher spread0.276 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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