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Record W2982178114 · doi:10.5539/ies.v12n11p94

From Mussels Stand to Becoming a Doctor: A Discussion on the Importance of Education in the Vertical Transition Between Social Strata in Terms of Career Choices and Sources of Vocational Awareness in Children with Low Socioeconomic Level

2019· article· en· W2982178114 on OpenAlexvenueno aff
Emel Tüzel İşeri

Bibliographic record

VenueInternational Education Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusVocational educationPsychologyContext (archaeology)Qualitative researchPresentation (obstetrics)PedagogyMedical educationSociologyPopulationSocial scienceMedicine

Abstract

fetched live from OpenAlex

This study aims to discuss the importance of education in vertical transition between social strata in the context of career choices and vocational awareness sources in children with low socioeconomic level. The study used a phenomenological design within a qualitative research approach framework. The study group consisted of 55 fourth-grade students enrolled in a primary school in a low socioeconomic area of Izmir province in Turkey. Findings of the research laid out that the students mostly wanted to be a doctor, their positive attitudes towards the profession and their desire to be beneficial to the society gained importance in their career choices, taking the example of people who they encountered in real life and who they watched on TV affected their career choices, and that there were participant views indicating that a thorough presentation of careers was not conducted even though a number of professions were mentioned in the lesson. The findings were discussed in terms of carrying out vocational guidance more carefully for participants consisting of children with low socioeconomic level who have parents with low education level so that its disadvantages can be eliminated.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.065
GPT teacher head0.364
Teacher spread0.299 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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