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Record W2995616613 · doi:10.22323/1.367.0091

Subatomic Physics Education and Outreach in Newfoundland

2019· article· en· W2995616613 on OpenAlexafffundabout
S. Barkanova

Bibliographic record

VenueProceedings of XXIX International Symposium on Lepton Photon Interactions at High Energies — PoS(LeptonPhoton2019) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOutreachIndigenousSubatomic particlePhysics educationScience educationVariety (cybernetics)Set (abstract data type)Mathematics educationPedagogySociologyPhysicsComputer scienceEcologyPolitical sciencePsychology

Abstract

fetched live from OpenAlex

A physics education provides students with a valuable and flexible skill set that opens doors to a wide variety of career paths, including industry, education and finance. However, Physics is almost universally unpopular in our local schools, especially among girls. This paper describes a long-term scientific and cultural outreach program we developed for youth in Newfoundland, Canada, especially rural youth, girls, and Indigenous students. We feature female and Indigenous role models, engage Indigenous storytelling, discuss a wide range of career opportunities, and emphasise a diverse set of skills required in modern science, such as cooperation and communication. As these students enter our post-secondary physics program, which is specifically developed to focus on subatomic physics and astrophysics, we engage them in research as early as their second year, making sure they are well prepared to continue at graduate schools in both theory and experiment.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.259
Teacher spread0.250 · 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.

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

Citations0
Published2019
Admission routes3
Has abstractyes

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