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Record W2014802516 · doi:10.5539/res.v7n4p54

Information and Logistic Foundations of Pedagogical Education Design and Content Education

2015· article· en· W2014802516 on OpenAlexvenueno aff
Vera K. Vlasova, Galia I. Kirilova, Alfiya R. Masalimova

Bibliographic record

VenueReview of European Studies · 2015
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
FundersKazan Federal University
KeywordsField (mathematics)Statement (logic)Computer scienceContent (measure theory)Information societyProfessional developmentEngineering ethicsMathematics educationSociologyKnowledge managementEngineering managementPedagogyPsychologyPolitical scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

The statement and solution of the design problem and implementation of pedagogical education content on the basis of information streams integration demands scientific and experimental justification which is carried out within the problem field of pedagogical science. In this regard, this article aims at developing an information-logistic model for designing and implementing the content of teacher education. The presented information-logistic model for designing and implementing the content of teacher education in the article presupposes the disclosure of the pedagogical system component structure as the base of information traffics differentiation. Materials of the article can be useful for the improvement of teachers’ training system focused on their professional growth and their future competitiveness in the conditions of information society and innovative production development.

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)0.007
metaresearch head score (Gemma)0.020
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 score0.010
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0020.013
Scholarly communication0.0100.012
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.586
GPT teacher head0.458
Teacher spread0.129 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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