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Record W2903067057 · doi:10.1080/07393148.2018.1528534

Breaking Down Barriers of Culture and Geography? Caring-at-a-Distance through Web 2.0

2018· article· en· W2903067057 on OpenAlexafffund
Roberta Hawkins

Bibliographic record

VenueNew Political Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGeneral partnershipPower (physics)CorporationSocial mediaPublic relationsWeb 2.0Political scienceSociologyThe InternetWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Abstract This article analyzes Join My Village (JMV), an NGO-corporation partnership that aims to “break down barriers of culture and geography” using the “power of online communities.” JMV uses Web 2.0 technologies to entice online users in the USA to engage with content about women’s lives in Malawi. Each time a user clicks on JMV content, the corporate partners donate money to the NGO. Using discourse analysis and interviews, I examine how JMV encourages users to care about distant others and with what effects. I draw attention to the use of Web 2.0 in the campaign in terms of how distant others become entangled in social media users’ everyday lives and the types of engagement JMV encourages. I conclude that while JMV offers some possibilities for caring-at-a-distance, the contradictory messaging and the corporate aspects of the campaign need more critical analysis.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.016
Scholarly communication0.0090.011
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.016
GPT teacher head0.331
Teacher spread0.314 · 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 designQualitative
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

Citations11
Published2018
Admission routes2
Has abstractyes

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