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Record W4387457461 · doi:10.21505/ajge.2023.0015

An Interview with Dr Rebecca D. Napier

2023· article· en· W4387457461 on OpenAlexaboutno aff
Marie Young

Bibliographic record

VenueAustralasian Journal of Gifted Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingGifted educationSociologyProject commissioningManagementTournamentLibrary sciencePedagogyMedia studiesPolitical scienceLaw

Abstract

fetched live from OpenAlex

Dr Rebecca D. Napier is a leading authority on giftedness, with a background in working with families and schools in Canada and Australia. She holds several degrees from American and Australian universities, and has a PhD in gifted education. Rebecca is currently undertaking postgraduate lecturing roles in gifted education at Flinders University. As the Director of Gifted Pathways, she is also a consultant and coach for schools and gifted families. She also recently held the position of Gifted Education Advisor to 103 South Australian Schools. Some of Rebecca’s other accomplishments include being a founding board member of Australia's first gifted school, and a board member of Australian gifted associations. Rebecca's practical experience and research findings have been supported by institutions including Flinders University, Australian Mensa, Debating SA, Chess School SA, Oliphant Science Awards, Tournament of Minds, ABC TV, and Life FM radio. Since 2001, Rebecca’s main mission has been the practical application of research into learning, development, and wellbeing for children. She has made a number of contributions to the Australasian Journal of Gifted Education (Napier & Halsey, 2022; Napier et al., 2023).

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)0.001
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 score0.529
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.363
Teacher spread0.327 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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