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Record W1604865358 · doi:10.15353/joci.v9i1.3193

Doing High-tech Collaborative Research in the Middle of Borneo:

2012· article· en· W1604865358 on OpenAlexvenueno aff
Sarah Hitchner

Bibliographic record

VenueThe Journal of Community Informatics · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsNexus (standard)Context (archaeology)InstitutionPoliticsLocal communityEthnographySociologyKnowledge managementPublic relationsPolitical scienceGeographyEngineeringSocial scienceAnthropologyComputer science

Abstract

fetched live from OpenAlex

This case study describes the experiences of an anthropologist currently conducting GIS-based ethnographic research in the Kelabit Highlands of Sarawak, Malaysia, using the e-Bario Telecentre as a local collaborating institution, a base for the input and storage of hard and soft copies of data and reports, and as a nexus for training community members to use GIS technology. Grounded in discussion of current collaborative research trends in the fields of anthropology and geography, this paper elaborates on the challenges and benefits of using the technology, facilities, and personnel currently available at the e-Bario Telecentre. It also describes how this current project is laying the foundation for a larger project that will be owned, managed, and used by the local community. This article elaborates on the social, cultural, political, economic, and environmental context in which this project is developing, demonstrating how this research project, and the transfer of technological knowledge that is a key component of it, can be both beneficial and challenging to the Kelabit community. Finally, it offers suggestions for the improvement of e-Bario by suggesting both what e-Bario can do to better serve the needs of researchers in the Kelabit Highlands and what researchers can in turn do to assist e-Bario in meeting its goals to serve the community, visitors, and other researchers.

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)0.014
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.176
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.097
GPT teacher head0.317
Teacher spread0.220 · 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.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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