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Towards an Interdisciplinary Socio-Technical Definition of Virtual Communities

2018· book-chapter· en· W4250971663 on OpenAlexaff
Umar Ruhi

Bibliographic record

VenueAdvances in computer and electrical engineering book series · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPerspective (graphical)Knowledge managementSociologyThe InternetTechnical communicationInformaticsEngineering ethicsManagement scienceData scienceComputer scienceEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

The objective of this chapter is to offer a holistic perspective of virtual communities (VCs) by outlining their underlying concepts and fundamental properties. Firstly, the chapter offers a brief synopsis of research fields that form the basis of socio-technical research on VCs. Key issues and theoretical orientations from four research streams are discussed, namely sociological/psychological, technological, business/management, and economic perspectives. Following this review, the chapter provides a summary of four interdisciplinary literature domains that have significantly contributed to the body of knowledge on VCs. These include computer-mediated communication, community informatics, knowledge management, and internet marketing. Definitions from seminal research studies in these domains are subsequently synthesized to propose an interdisciplinary socio‐technical definition of VCs. The proposed definition offers a nascent ascriptive characterization of VCs along five dimensions of participants, purpose, platforms, protocols, and persona, together constituting the 5 Ps of VCs.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0030.015
Scholarly communication0.0090.014
Open science0.0020.006
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.282
Teacher spread0.264 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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