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Record W4200396026 · doi:10.32370/ia_2021_12_3

Features of Pedagogical Technology of Teaching and Training of Police Officers in National Academy of Internal Affairs

2021· article· en· W4200396026 on OpenAlexvenueno aff
Lidiya Kotlyarenko, Olha Nesen, Olena Chuprina, Nataliia Pavlovska, Olena Kofanova

Bibliographic record

VenueIntellectual Archive · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary, Security, and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChristian ministryInstitutionService (business)Order (exchange)Student affairsPolitical sciencePedagogyTraining (meteorology)Medical educationHigher educationEmpirical researchPsychologySociologyPublic relationsBusinessLawMedicineGeography

Abstract

fetched live from OpenAlex

The article shows some aspects of pedagogical technologies for training police cadets. Analyzed the Peculiarities of professional pedagogical activity in institution of higher education of Ukraine with special conditions of studying. In the process of writing the article (research) was analyzed the similar world experience of pedagogical activity of colleagues from similar educational institutions of other countries. Have been used different types of research, methods and current empirical data. In order to update the research, all explorative groups of cadets were differentiated by age, gender and educational characteristics (previous service in the Ministry of Internal Affairs or the Ministry of Defense of Ukraine, availability of special education, etc.)

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.093
GPT teacher head0.395
Teacher spread0.302 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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