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Research Communities in Context

2010· book-chapter· en· W2480281607 on OpenAlexaff
Dimitrina Dimitrova, Emmanuel Koku

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsYork University
Fundersnot available
KeywordsAgency (philosophy)Context (archaeology)Public relationsFace (sociological concept)Work (physics)Government (linguistics)Knowledge managementBusinessDisciplineSociologyPolitical scienceEngineeringGeographySocial scienceComputer science

Abstract

fetched live from OpenAlex

This chapter examines a community of professionals, created by a government agency and charged with conducting country-wide, cross-disciplinary, and cross-sectoral research and innovation in the area of water. The analysis describes the structure of the community and places it in the context of existing project practices and institutional arrangements. Under challenging conditions, the professionals in the area recruit team members from their trusted long-term collaborators, work independently on projects, use standard communication technologies and prefer informal face-to-face contacts. Out of these practices emerge a sparsely connected community with permeable boundaries interspersed with foci of intense collaboration and exchange of ideas. In this community, professionals collaborate and exchange of ideas with the same colleagues. Both collaboration and exchanges of ideas tend to involve professionals from different disciplines and, to a lesser extent, from different sectors and locations.

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)0.015
metaresearch head score (Gemma)0.019
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 score0.025
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0130.031
Scholarly communication0.0250.026
Open science0.0030.025
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0160.003

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 head0.118
GPT teacher head0.374
Teacher spread0.256 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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