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Record W1513375119 · doi:10.15353/joci.v7i3.2599

Research informing practice: Toward effective engagement in community ICT in New Zealand

2011· article· en· W1513375119 on OpenAlexvenueno aff
Barbara J. Craig, Jocelyn Williams

Bibliographic record

VenueThe Journal of Community Informatics · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsParticipatory action researchCitizen journalismInformation and Communications TechnologySociologyPublic relationsAction (physics)Power (physics)Action researchSocial mediaParticipatory cultureParticipant observationSense of communityCommunity of practiceSocial scienceMedia studiesPolitical sciencePedagogyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

New Zealand’s Computers in Homes has been researched since its inception in 2000, through both participatory action research and multiple mixed methods case studies, by the authors of this paper who are now collaborating to find the most meaningful way to assess social outcomes in the scheme as it evolves. Computers in Homes (CIH) not only continues to be informed by the research but it is also beginning to make use of social media for community participant engagement. This paper traces the inter-relationship between the ongoing research and evolution of practice, reflecting on a shift in epistemology and thus research design. Our work now extends to explore the relationship between community blogging, adopted by CIH as a way of engaging the community in making sense of their own experience and thus owning their own research, and the role of social relationships in facilitating a sense of belonging. Our paper examines how the use of social media in this way may challenge the more traditional ideas and power relations inherent in the researcher-participant relationship in community ICT 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)0.040
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0400.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.007
Insufficient payload (model declined to judge)0.0000.000

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 head0.154
GPT teacher head0.357
Teacher spread0.204 · 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 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

Citations7
Published2011
Admission routes1
Has abstractyes

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