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Record W2010104772 · doi:10.5539/jsd.v8n4p95

The Teachers’ Preparation for the Work with Deviancy-Prone Students

2015· article· en· W2010104772 on OpenAlexvenueno aff
Olesya Babenko

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsnot available
FundersKazan Federal University
KeywordsMainstreamConstructiveSet (abstract data type)Construct (python library)PsychologyWork (physics)Promotion (chess)Mathematics educationSocial psychologyPedagogyPoliticsComputer science

Abstract

fetched live from OpenAlex

The problem investigated sounds acute as long as any teacher can meet a necessity to work with different categories of students including young men and ladies who are at war with rules and regulations, and do not want to put up with typical social behaviour. The article aims at defining deviant behaviour as a studying discipline violation, which can become a harsh obstacle for the teacher’s attempts to construct a usual architecture of a lesson. It also reveals a set of psychological-and-pedagogical conditions which are seen as capable of overcoming students’ deviant behaviour. The experiment underlining the effectiveness level of the complex (set) suggested has become a mainstream investigation method of this problem and heads for a constructive work with risk-prone students (prone to deviant behaviour). Following a step-by-step prevention of students’ deviant behaviour, implementing preventive pedagogics and deviantology into courses of further promotion for teachers, constructing theatre studios for risk-prone students – all this can set up a healthy atmosphere in a students’ group and become a strong basis of preventive knowledge for the teacher. The article materials can be useful while organizing the teacher’s psychological-and-pedagogical work (especially for beginning teachers) with a collective of risk-prone, deviant-behaving students – the students who tend to ruin the studying discipline systematically.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.039
GPT teacher head0.356
Teacher spread0.316 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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