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Record W2958206407 · doi:10.29173/iasl7225

RESEARCH NOTEBOOKS Developing Students' Critical Thinking Skills in KAETSU ARIAKE Junior & Senior High School library

2016· article· en· W2958206407 on OpenAlexvenueno aff
Shoko Sanada, Hideo Yamada, Aiko So, Terumi Kuwata

Bibliographic record

VenueIASL Annual Conference Proceedings · 2016
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacy and Medical Practices
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationVariety (cybernetics)Critical thinkingBloom's taxonomyPsychologyMedical educationLibrary sciencePedagogyComputer scienceMedicineCognition

Abstract

fetched live from OpenAlex

We, Kaetsu Ariake, regard 21st century skills as one of the most important factors for our students to acquire. Especially, critical thinking (CT) and its skills are indispensable. Thus, we offer special classes for the 7th, 8th, and 9th grade students to become better thinkers. Teachers from a variety of subjects teach the classes, which are held in our school library with the help of its librarian. The librarian teaches the students library skills, supports the teachers, and edits the Research Notebooks. The Research Notebooks are the key to managing the three-year course. We refer to the six-step learning program and Revised Bloom’s Taxonomy in order to develop the Research Notebooks. Using the Notebooks, the students can develop good insights and skills to do research. By making use of the library, getting the help of the librarian, and using the Research Notebooks, we promote 21st century skills throughout our school.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0290.007

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 head0.184
GPT teacher head0.516
Teacher spread0.332 · 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 designQualitative
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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