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Record W2273193417

과학적 소양에 기반한 과학과 성취기준의 개발 방향 탐색

2015· article· ko· W2273193417 on OpenAlexaboutno aff
백남진

Bibliographic record

Venue교육과학연구 · 2015
Typearticle
Languageko
FieldSocial Sciences
TopicEducation, Safety, and Science Studies
Canadian institutionsnot available
Fundersnot available
KeywordsScientific literacyCurriculumScience educationLiteracySociology of scientific knowledgeSet (abstract data type)Mathematics educationScience, technology, society and environment educationNational Science Education StandardsEngineering ethicsPedagogySociologyPolitical sciencePsychologyComputer scienceHigher educationSocial scienceEngineeringComparative education
DOInot available

Abstract

fetched live from OpenAlex

This study aimed to identify the characteristics of scientific literacy-based standards and to explore ways to develop scientific literacy-based standards in a science curriculum. To that end, this study first reviewed the definitions of scientific literacy and the revised 2009 Korean national science standards. S econd, i t reviewed t he s cience s tandards i n science curricula o f Canada(Ontario), Australia, and Singapore. Finally, it drew implications from these nations cases. This study proposed several ways to develop scientific literacy-based standards. First, in order to set science standards, the definition and constructs of scientific literacy should be identified. Second, science standards should consider the multifaceted nature of scientific literacy. Third, science standards should present the big ideas, skills, and attitudes of science that students are expected to gain from science learning. Finally, science standards should present the connections of inter-areas(knowledge, skills, and attitudes), inter-knowledge, and inter-strand areas.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.608
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.136
GPT teacher head0.409
Teacher spread0.273 · 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.

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
Published2015
Admission routes1
Has abstractyes

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