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Record W133080853 · doi:10.1007/978-94-6091-506-2_9

Enhancing Science Education Through an Online Repository of Controversial, Socioscientific News Stories

2011· book-chapter· en· W133080853 on OpenAlexaff
Susan M. Teed David B. Zandvliet, Carlos G. A. Ormond

Bibliographic record

VenueSensePublishers eBooks · 2011
Typebook-chapter
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsScientific literacyRelevance (law)Resource (disambiguation)Science educationLiteracyInformation literacyEngineering ethicsMedia literacyPedagogySociologyMathematics educationPolitical sciencePsychologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Scientific literacy involves the engagement of authentic science, technology, and environment issues by applying scientific knowledge and fundamental literacy (Yore, Chapter 2 this book). This project and its series of studies explored how students and teachers responded to an innovative instructional resource—Science Times—designed on contemporary and controversial issues. The importance of this effort is to establish relevance for science and environmental education by using contemporary, local news issues to challenge students and the related teaching approach to critically engage students. Case studies of the resource and teaching approaches are used to document student and teacher learning and reactions. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0060.011
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.004

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 head0.097
GPT teacher head0.354
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; 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

Citations2
Published2011
Admission routes1
Has abstractyes

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