Bibliographic record
Abstract
Bruce M. Shore’s research contributions in gifted education have focused on three contexts that impact how giftedness is understood and the instructional environments that serve gifted learners’ educational needs. This article describes these contributions and provides selected examples plus a more complete Supplemental Online bibliography. First, giftedness benefits from being conceptualized in terms of theories that address the development of expertise. Featured expert–gifted parallels include interconnectedness of knowledge, metacognitive processes, perspective taking, active learner roles, affinity for novelty and complexity, and task representation and planning. Illustrative research is described from preschool age through higher education, including connections to creativity research. Second, gifted education benefits when guided by social-constructivist theory of education and its expression in inquiry-based instruction. Examples include building upon learner interests, question asking, collaborative inquiry, and active learner roles. Desirable specific instructional practices are framed by the above theories and by being considered in the contexts of widely recommended and best practices with their research support. Third, gifted education, at all levels including higher education and teacher education, needs to be an integral part of the context of general education. Most specific gifted education practices also work in general education, including learning high-level skills within subject matter. Nineteen examples are cited about how gifted education contributes to the quality of general education.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".