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Record W2067967228 · doi:10.4018/jvcsn.2010100101

Managing Collaborative Research Networks

2010· article· en· W2067967228 on OpenAlexaff
Dimitrina Dimitrova, Emmanuel Koku

Bibliographic record

VenueInternational Journal of Virtual Communities and Social Networking · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsYork University
Fundersnot available
KeywordsSocial network analysisFunction (biology)Knowledge managementOnline and offlineFace (sociological concept)Dual (grammatical number)Social network (sociolinguistics)Online communityPublic relationsComputer scienceSocial mediaSociologyWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

This paper explores how management practices shape the way dispersed communities of practice (CoPs) function. The analysis is a case study of a dispersed community engaged in conducting and managing collaborative research. The analysis uses data from a social network survey and semi-structured interviews to capture the management practices in the community and demonstrate how they are linked to the patterns of information flows and communication.This analysis is a test case for the broader issue of how distributed communities function. It shows that even highly distributed CoPs may have a dual life: they exist both online and offline, in both face-to-face meetings and email exchanges of their participants. The study examines a dispersed community engaged in conducting and managing collaborative research. The analysis uses data from a social network survey and interviews to examine its managerial practices, information exchanges and communication practices.

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)0.061
metaresearch head score (Gemma)0.160
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.160
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.005
Science and technology studies0.0100.006
Scholarly communication0.0160.023
Open science0.0060.028
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0110.004

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 head0.075
GPT teacher head0.398
Teacher spread0.323 · 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.

Study designNot applicable
DomainMethods
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

Citations18
Published2010
Admission routes1
Has abstractyes

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