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Record W2595701487 · doi:10.22323/1.276.0115

STEAM: Education and Communication with Art at ATLAS and CMS

2016· preprint· en· W2595701487 on OpenAlexfundno aff
P. Paolucci, Angelos Alexopoulos, M. Hoch, Carole Adam

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsnot available
FundersHigh Energy PhysicsEuropean CommissionCERNTRIUMF
KeywordsOutreachThe artsSocial connectednessPublic relationsEngineering ethicsPolitical scienceMathematics educationEngineeringPsychology

Abstract

fetched live from OpenAlex

Recent developments in science education policy and practice suggest that successful learning in the 21st century requires the horizontal connectedness across areas of knowledge by linking the arts and humanities with science, technology, engineering and mathematics (STEM) subjects. The rapidly increasing STEAM movement calls for arts integration into science teaching and learning to help school students develop skills that are necessary to thrive in an innovation economy. Education and outreach in highenergy physics are not an exception to these developments. This paper describes specific education and outreach initiatives by the ATLAS and CMS collaborations that use a cross-disciplinary approach to engaging the public and especially young people not only with the excitement of scientific research in particle physics but also with its positive technological and social externalities.

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)0.005
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.197
Threshold uncertainty score0.658

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0090.004
Open science0.0010.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1970.029

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 head0.064
GPT teacher head0.422
Teacher spread0.358 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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