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Record W4283119787 · doi:10.5539/jel.v11n4p153

Values Lost in Society in the Eyes of Teachers

2022· article· en· W4283119787 on OpenAlexvenueno aff
Sevgi Koç

Bibliographic record

VenueJournal of Education and Learning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicValues and Moral Education
Canadian institutionsnot available
Fundersnot available
KeywordsFamily valuesHonestyPsychologyValues educationValue (mathematics)PedagogySocial psychologyReligious valuesSociologyLaw

Abstract

fetched live from OpenAlex

People’s actions are governed by their values, beliefs, perspectives, and world views. There is so much diversity that everyone has or creates their own moral standards, principles, and values, making social cohesion difficult. We come to a dead-end when we evaluate different moral standards and value systems from the perspective of relativity and absoluteness. People acquire values first in the family and then at school. Therefore, teachers play a crucial role in helping students adopt values and moral standards. This study investigated teachers’ views on values (especially lost values). This study adopted a qualitative research design (phenomenology). Data were collected using a semi-structured interview questionnaire developed by the researcher. The questionnaire consisted of six questions. The study sought answers to the following questions: What do teachers think about values and values education? What values do teachers think we have lost in society? Why do teachers think we have lost values in society? and How do teachers think we can get back the lost values? Participants viewed values as moral principles. They regarded values education as the type of education required for society to live in a healthy, harmonious, and peaceful way. They thought that we had lost the values of honesty, understanding, respect, etc. They believed that we had lost those values because of social media, changes in the institution of family, etc. They suggested that we provide students with values education courses and encourage school-family collaboration to get back the lost values.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.147

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 studies0.0000.000
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 head0.030
GPT teacher head0.380
Teacher spread0.350 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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