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Record W2797379986 · doi:10.1080/02783193.2018.1434710

Science and Technology Education in Slovenian Compulsory Basic School: Possibilities for Gifted Education

2018· article· en· W2797379986 on OpenAlexaff
Gregor Torkar, Stanislav Avsec, Mojca Čepič, Vesna Ferk Savec, Mojca Juriševič

Bibliographic record

VenueRoeper Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsJuvenile Diabetes Research Foundation
Fundersnot available
KeywordsSyllabusOperationalizationCurriculumPsychologyMathematics educationScience educationCompulsory educationGifted educationPedagogySelection (genetic algorithm)Medical educationMedicine

Abstract

fetched live from OpenAlex

The article presents an analytical overview of the science and technology curriculum from the viewpoint of the inclusive approach adopted toward gifted education in Slovenian basic education. The main research question concerns how the current curriculum fits the learning needs of gifted students. For the purposes of the study, 16 compulsory and elective syllabi of science and technology school subjects were identified and qualitatively analyzed, and the role of activity days was examined within the target framework. The results show a rather weak operationalization of recommendations for gifted education in defined learning objectives and standards in the syllabi. Moreover, it was found that elective school subjects in science and technology are poorly represented in students’ overall selection of elective school subjects. In addition, activity days offer numerous possibilities for the implementation of the general recommendations for teaching the gifted.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.344
Teacher spread0.323 · 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 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

Citations11
Published2018
Admission routes1
Has abstractyes

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