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Record W4293253414 · doi:10.34190/eckm.23.2.465

Semiotic Inception, Attitude Altering, and Behavioral Expression: Understanding the Foundation of Organizational Knowledge Construction

2022· article· en· W4293253414 on OpenAlexaff
Chulatep Senivongse, Alex Bennet

Bibliographic record

VenueEuropean Conference on Knowledge Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Identity and Reputation
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsSemioticsSign (mathematics)Foundation (evidence)Expression (computer science)PsychologyEpistemologyCognitionSpace (punctuation)Cognitive scienceKnowledge managementCognitive psychologyComputer scienceSocial psychologySociologyMathematicsPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

This study explores the theory of semiotics and how it is processed in the cognitive space of a person (a complex adaptive system) with a focus on an individual’s response to persuasive arousal, how behavior is altered, and how habits are formulated. The study involves reviewing on the theory of semiotics, attitude altering, and behavior enactment. An SIAB framework is constructed from the combination of multiple fields of knowledge domains. The proof of the framework construction validity is verified by systematic literature review and meta-analysis techniques on the past marketing semiotic research. The framework can explain how humans incept the sign, how the sign influences attitudes, and how behavior is expressed. The SIAB framework can be the foundation to explain how individual knowledge is constructed, which can support many future studies.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0020.024
Scholarly communication0.0080.012
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.271
Teacher spread0.199 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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