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Record W3165762378 · doi:10.5539/jms.v11n2p15

Definition of Trust as a Catalyst and the Implications Therefrom: A Deduction from a Literature Review

2021· review· en· W3165762378 on OpenAlexvenueno aff
Peter King

Bibliographic record

VenueJournal of Management and Sustainability · 2021
Typereview
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsConfusionExpress trustBlind trustUniversality (dynamical systems)Focus (optics)VerbComputer scienceKnowledge managementEpistemologyPsychologySociologyPublic relationsPolitical scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Much has been written about trust and trust-building, but no consensual definition of trust has been developed. In this article, the definition of trust as a catalyst is proposed based on a deduction from an aggregation of peer-reviewed articles from across several disciplines and hermeneutic examination of the contents. The paper suggests that discipline-related points of view and common usage of trust as a noun and or a verb leads to confusion in trying to develop a consensual definition. Given the accepted universality of trust, a consensual definition would help achieve a further understanding of both trust and trust-building. The proposed definition permits recognition of discipline-related definitions and suggests the focus of trust should be directed to establishing the conditions under which trust enables successful exchange interactions (i.e., trust-building). The separation of trust and trust-building has implications for management and other relationships. Suggestions for further research are included.

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)0.006
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0120.012
Science and technology studies0.0010.002
Scholarly communication0.0040.008
Open science0.0010.002
Research integrity0.0020.003
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 head0.028
GPT teacher head0.345
Teacher spread0.317 · 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
GenreReview

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

Citations2
Published2021
Admission routes1
Has abstractyes

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