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Record W2592241841 · doi:10.5430/ijhe.v6n2p43

The Roles of Religious Culture and Moral Knowledge Teachers in Organizing Their Students Relationships with Social Networks

2017· article· en· W2592241841 on OpenAlexvenueno aff
Emine Zehra Turan, Gökçe Becit İşçitürk

Bibliographic record

VenueInternational Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Character Development
Canadian institutionsnot available
Fundersnot available
KeywordsRaising (metalworking)The InternetPsychologySocial responsibilityMoral educationSocial psychologyPeriod (music)SociologyPedagogyPublic relationsPolitical scienceAestheticsComputer science

Abstract

fetched live from OpenAlex

In parallel to the improvements experienced in information and communication systems in recent years, any use of Internet, especially the social networks by children and adolescents has been noticed to be increasing gradually. Use of social networks that starts at early ages has exposed children to some dangers. For that reason, the responsibility for teachers and parents upon raising the awareness of students for the threats possible to be encountered on the internet and upon being a guide and role model appears. As in other different branches, the role of Religious Culture and Moral Knowledge (RCMK) teachers upon being a guide and role model has also gradually increased. Subsequent to this study, needs of Religious Culture and Moral Knowledge teachers on this issue could be determined, and necessary seminars could be prepared. In this period when we live with social networks, it seems important in terms of teachers to raise the awareness and be aware of their responsibilities on their students.

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)0.003
metaresearch head score (Gemma)0.005
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.372
Teacher spread0.338 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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