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Record W2904524395 · doi:10.32370/2018_11_12

The Technology for Forming Professional Reflection in Future Social Work Educators in a Higher Educational Institution

2018· article· en· W2904524395 on OpenAlexvenueno aff
Nadezhda Chernukha, Maryna Vasylyeva-Khalatnykova, Liudmyla Tokaruk

Bibliographic record

VenueIntellectual Archive · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Social Development in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsOperationalizationInstitutionReflection (computer programming)Engineering ethicsWork (physics)Social workProcess (computing)Set (abstract data type)Professional developmentHigher educationSociologyPedagogyKnowledge managementPublic relationsEngineeringPolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

The article analyzes the technology for forming professional reflection in future social work educators in a higher education institution. Given technology is described as scientific and theoretical basis for the optimal implementation of the tasks of professional training. This technology is considered as a set of regular, functionally related components which presents an integral system. Selected components in technology are divided into some blocks (goal-oriented, functional, informative, organizational, productive). This components provide an opportunity to represent more clearly the purposeful process of forming professional reflection in future social work educators in the higher education institution. They highlighted how social work educators' future work in agencies help them to operationalize social work values and ethics, develop an awareness of themselves as social workers, and enhanced their confidence.

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.004
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.045
GPT teacher head0.382
Teacher spread0.337 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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