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Record W3192928511 · doi:10.1145/3445793

Rural Uncommoning

2021· article· en· W3192928511 on OpenAlexfundno aff
Nicola J. Bidwell

Bibliographic record

VenueACM Transactions on Computer-Human Interaction · 2021
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
FundersEuropean CommissionInternational Development Research Centre
KeywordsCommonsLivelihoodIdentity (music)Corporate governanceEthnographyRural communityPolitical scienceSociologyRural societyRural areaEconomic growthGeographySocioeconomicsBusinessAnthropologyLawEconomics

Abstract

fetched live from OpenAlex

Shared use of small-scale natural commons is vital to the livelihoods of billions of rural inhabitants, particularly women, and advocates propose that local telecommunications systems that are oriented by the commons can close rural connectivity gaps. This article extends insights about women's exclusion from such Community Networks (CNs) by considering ‘commoning’, or practices that produce, reproduce and use the commons and create communality. I generated data in interviews and observations of rural CNs in seven countries in the Global South and in multi-sited ethnography of international advocacy for CNs. Male biases in technoculture and rural governance limit women's participation in CNs, and women adopt different approaches to performing their communal identity while using technology. This situation contributes to detaching CNs from relations that are produced in women's commoning. It also illustrates processes that co-opt the commons in rural technology endeavours and the diverse ways commoners express their subjectivities in response.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0440.004

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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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