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Record W2041166561 · doi:10.12735/ier.v2i2p44

Changing Institutional Research Productivity of United States’ Science Education Programs: 2000s v. 1990s

2014· article· en· W2041166561 on OpenAlexvenueno aff
Lloyd H. Barrow

Bibliographic record

VenueInternational Education Research · 2014
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityPolitical scienceScience educationEconomic growthEconomicsLaw

Abstract

fetched live from OpenAlex

The purpose of this study was to identify the major science education programs in the United States, where the science education researchers published their research during the first decade of the twenty-first century. This research study of the scholarly productivity of science education programs at United States’ institutions of higher education is compared with the last decade of 1990s (Barrow, Settlage, & Germann, 2008). Each issue of the eight research journals (Journal of Research is Science Teaching, Science Education, International Journal of Science Education, Journal of Science Teacher Education, School Science and Mathematics, Journal of Computers in Math and Science Teaching, Journal of Science Education and Technology, and Journal of Elementary Science Education) published in the 2000s provided the author(s) and their institutional affiliation. The resultant ranking of raw and weighted counts for the top 30 U.S science educations programs shows variation in journals where research was published. There were almost 50% of the 2000s top 30 institutions which were not listed for the 1990s. Potential explanations for variations and uses for ranking are discussed.

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)0.135
metaresearch head score (Gemma)0.151
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, Scholarly communication, Open science
Consensus categoriesMetaresearch, Bibliometrics, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1350.151
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.1560.244
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0050.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.693
GPT teacher head0.680
Teacher spread0.013 · 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 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
Published2014
Admission routes1
Has abstractyes

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