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Record W3021087396 · doi:10.5267/j.msl.2020.4.031

Factors affecting knowledge sharing behavior of lecturers: The case of public universities

2020· article· en· W3021087396 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge sharingKnowledge managementBusinessPsychologyComputer scienceMathematics education

Abstract

fetched live from OpenAlex

Studying knowledge sharing behavior of lecturers at public universities in Ho Chi Minh City is an urgent issue. In the development and vastness of knowledge treasure, knowledge sharing actually becomes a way to reduce the difficulties and waste of time to learn, acquire the knowledge, thereby, helping public universities build a team of lecturers who have good knowledge and meet the job requirements for their development. Therefore, the author conducts a study to bring out the factors affecting activities of knowledge sharing behavior of lecturers at public universities in Ho Chi Minh City now. Based on the data collected, we use Cronbach's Alpha, EFA and run regression model for knowing the impact levels of each independent variable on dependent variable of the knowledge sharing behavior of lecturers. Based on the findings, some recommendations are given for improving the knowledge sharing behavior of lecturers at public universities in Ho Chi Minh City.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.310
Teacher spread0.235 · 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