Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".