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Record W4313730353 · doi:10.5539/ies.v16n1p78

The Views of Secondary School Students on Entrepreneurship-Assisted Science Course

2023· article· en· W4313730353 on OpenAlexvenueno aff
Gizem Turan Gürbüz, Murat Aydın

Bibliographic record

VenueInternational Education Studies · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupEntrepreneurshipQualitative researchPsychologyMathematics educationContent analysisMedical educationPedagogyMultimethodologyQualitative propertySociologyMedicineSocial sciencePolitical science

Abstract

fetched live from OpenAlex

The present study aims to investigate the views of secondary school students on an entrepreneurship-assisted science course. The study was carried out with the case study method, one of the qualitative research methods. The study data were collected with qualitative research techniques such as focus group interviews and diaries. The study group included 23 5th-grade students attending a secondary school located in Adıyaman urban center during the 2020-2021 academic year. The data were analyzed with content analysis. The study findings demonstrated that students considered the entrepreneurship-assisted science course and related practices as fun and interesting in the focus group interviews. Similarly, the diary entries revealed that the entrepreneurship-assisted science course and the activities were enjoyable according to the students and they liked these activities. Furthermore, it should be noted that the students mentioned entrepreneurship dimensions in both focus group interviews and diaries.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.075
GPT teacher head0.399
Teacher spread0.324 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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