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Record W4288345631 · doi:10.5281/zenodo.3989472

Entrepreneurship Learning Based on Literacy Skill Conservation

2019· article· en· W4288345631 on OpenAlexaff
Ihat Hatimah, Yeti Mulyati, Kuswara Kuswara

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicVocational and Entrepreneurial Education
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsLiteracyEntrepreneurshipMathematics educationPsychologyPedagogyComputer scienceBusiness

Abstract

fetched live from OpenAlex

Basic literacy comprising reading, writing, and arithmetic skills that have been possessed by one illiterate must be maintained and improved so that relapsing does not take place. In other sides, the learning of literacy for adults’ learners must be meaningful, giving economical and living values enabling the learners to improve their living standards through entrepreneurship education oriented to culture and local potential. These two combinations are the bases for the choice of education model in this study and is called entrepreneurship education based on literacy program. By combining these two aspects in its implementation, the learners are expected to be able to maintain and improve their possessed literacy and their entrepreneurship ability based on culture and local potential. This study employed descriptive analytical method. Involving 100 learners from three groups of Community Learning Centers in Pantura area, including Community Learning Center of Family Indramayu Regency, Community Learning Center of Bina Kreatif Bahari of Cirebon Regency, and Community Learning Center of Bima Sakti of Subang Regency as the source of data, this study revealed that entrepreneurship education based on literacy program could improve the ability of learners in reading, writing, and arithmetic.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.013

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 head0.032
GPT teacher head0.289
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicVocational and Entrepreneurial EducationFrench-language works237,207