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Pedagogical Conditions for Ensuring the Quality of Engineering Training in Ukraine in the 19th Century

2020· article· en· W3109882687 on OpenAlexvenueno aff
Вячеслав Олексенко

Bibliographic record

VenueEncounters in Theory and History of Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Education Studies Worldwide
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Modernization theoryCurriculumNormativeQuality (philosophy)Context (archaeology)Process (computing)EstateEngineering ethicsReal estatePolitical scienceEngineering managementSociologyPublic relationsEngineeringPedagogyComputer scienceLawGeography

Abstract

fetched live from OpenAlex

This article discusses the research results of the didactic system of training engineers at the Kharkiv Practical Technological Institute (Ukraine). It establishes the historical context for the founding of the first technical institute on the Left-Bank of Ukraine, including accounting for the great need for civilian private enterprises in engineering personnel, as well as the influence of businessmen. In carrying out the institutional analysis of the basic organizational and normative documents that regulate the educational process, particular attention is paid to class schedules and curricula from the opening of the institute in 1885 to 1891. The article comprehensively outlines the components of ensuring the quality of engineering training and considers the modernization of the process of training engineers. It also identifies and substantiates information parameters of the students, including the social status of future engineers by estate and faith. This research produces a holistic understanding of student training and expulsion. The research results are based on archival materials and literature of the 19th century.
 

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 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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.121
GPT teacher head0.405
Teacher spread0.284 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

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