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Record W3047232728 · doi:10.25071/1916-4467.40452

Understanding the Use of Academic Research in Science Education Practitioner Journals

2020· article· en· W3047232728 on OpenAlexaffvenue
Michael Bowen, Joseph Taylor, Patricia G. Patrick, Ryan Summers, Marcus Kubsch, Abdi-Rizak M. Warfa, Asli Sezen‐Barrie, Selcen Güzey, Cathy Lachapelle

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsMount Saint Vincent University
FundersLeibniz-Gemeinschaft
KeywordsCitationScience educationPresentation (obstetrics)SociologyLibrary sciencePedagogyMedicineComputer science

Abstract

fetched live from OpenAlex

This bibliometric study investigated the extent to which science education practitioner journals (SEPJs) cite science education research journals and other resources, and in what proportions. The study found that articles in SEPJs rarely cite the leading science education research journals; the average citation rate per article is well below one. This result was not affected by article type and remains stable across 2013 to 2017. While results indicate the article purpose in the SEPJs affected the proportion of science education research journal citations, the proportion remains low with—in the best case—about 8% of all citations in The Science Teacher from 2013 to 2017. The presentation discusses the role of SEPJ authors of different roles/backgrounds in science education and their use of references. Implications for pedagogical content knowledge (PCK) development and the translation of research to practice are described.

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)0.045
metaresearch head score (Gemma)0.339
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.339
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0600.066
Science and technology studies0.0020.003
Scholarly communication0.0160.014
Open science0.0020.006
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.684
GPT teacher head0.542
Teacher spread0.143 · 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.

Study designObservational
DomainEvaluation
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
Published2020
Admission routes2
Has abstractyes

Explore more

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