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Record W1553258822 · doi:10.22230/cjc.2003v28n3a1373

The Progressive Construction of Communication: Toward a Model of Cognitive Networked Communication and Knowledge Communities

2003· article· en· W1553258822 on OpenAlexafffundvenue
Milton Campos

Bibliographic record

VenueCanadian Journal of Communication · 2003
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversité de Montréal
FundersSimon Fraser UniversityUniversidade de São PauloUniversity of TorontoMcGill UniversityUniversité Laval
KeywordsPathosInterpersonal communicationEthosCognitionIntentionalityModels of communicationPsychologySocial psychologySociologyCognitive psychologyCognitive scienceKnowledge managementEpistemologyComputer scienceCommunicationPolitical scienceLinguistics

Abstract

fetched live from OpenAlex

Networked communication allows the emergence of a new social reality: knowledge communities in cognitive networks. This paper formulates a model of cognitive networked communication and knowledge communities. This model aims to show the progressive construction of communication and is based on an integrative view of conviviality (participation and engagement) and knowledge building(intentionality, individual and social representations that shape different levels of collaboration and influence learning in interpersonal relationships). The model suggests communication can progress from lower to higher cognitive levels, and that those levels are apparent in different types of communities. Although community communication is essentially structured through cognitive procedures ( logos), the model also suggests that it is shaped by affectivity and emotions ( pathos), and moral and cultural values, beliefs, and opinions ( ethos).

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)0.003
metaresearch head score (Gemma)0.006
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: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0040.023
Scholarly communication0.0070.015
Open science0.0020.005
Research integrity0.0030.003
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 head0.087
GPT teacher head0.372
Teacher spread0.285 · 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
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

Citations13
Published2003
Admission routes3
Has abstractyes

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