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Record W1982452625 · doi:10.1029/2006eo240003

Education and science connect at sea

2006· article· en· W1982452625 on OpenAlexfundno aff
R. Mark Leckie, Kristen St. John, Leslie Peart, A. Klaus, Scott Slough, M. D. Niemitz

Bibliographic record

VenueEos · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
FundersUniversity of AlbertaUniversity of Michigan
KeywordsScientific literacyScience educationPolitical scienceSpace SciencePublic relationsScience, technology, society and environment educationEarth system scienceLearning standardsSociologyEngineering ethicsLibrary scienceEngineeringComputer scienceLawEcology

Abstract

fetched live from OpenAlex

In the past several decades, the scientific community's collective understanding of Earth's history and the processes that shape this dynamic planet has grown exponentially. Yet communicating the current understanding of Earth systems to the community outside of science (educators and students, policy makers, and the general public) has lagged. In 1995, the U.S. National Academy of Sciences (NAS) led the effort to establish National Science Education Standards (http://www.nap.edu/readingroom/books/nses/),with the goal of helping all students achieve scientific literacy. Earth and space sciences are one of the eight categories of content standards. Clearly the establishment of science education standards alone will not foster a scientifically literate society, as indicated in the NAS report “Rising Above the Gathering Storm” (http://www.nap.edu/catalog/11463.html). This report, released last fall, warns that without strong steps to improve federal support for science and technology research, and science and technology education, the quality of life in the United States is threatened as the country loses its competitive edge.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.190
Teacher spread0.187 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2006
Admission routes1
Has abstractyes

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