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Record W2771294419 · doi:10.5296/gjes.v3i2.12107

Interest in Science: A Comparative Analysis of the Aims of School Science Syllabi

2017· article· en· W2771294419 on OpenAlexaboutno aff
Davis Jean Baptiste, David Palmer, Jennifer Archer

Bibliographic record

VenueGlobal Journal of Educational Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusCurriculumScience educationPolitical scienceMathematics educationSociologyPedagogyPsychology

Abstract

fetched live from OpenAlex

There is currently worldwide concern about the decline in students’ interest in science. The purpose of this paper is to examine school science curriculum documents to determine whether they explicitly state that they aim to enhance student interest in science. A document analysis was used to compare science syllabi from Canada, Finland, Sweden, UK, USA, Hong Kong, Thailand, Singapore, the Eastern Caribbean states, and Hungary. It was found that about half of these countries did have an explicit aim of enhancing student interest in science at high school level. The remainder either did not have any such aim, or had a partial or implied expectation. These findings were then compared to the results of the international PISA 2006 survey of student interest in science. It was found that those countries in which science curriculum documents made no mention about enhancing student interest (Canada and UK) were below the OECD average for interest in science. However, there were other countries below the OECD average, that did have a curriculum aim to enhance student interest in science. It is concluded that having a stated aim to enhance student interest in science is not enough, by itself, to bring that aim to fruition.

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)0.004
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.010
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.306
GPT teacher head0.562
Teacher spread0.257 · 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 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
Published2017
Admission routes1
Has abstractyes

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