Giftedness Is Not What It Used to Be, School Is Not What It Used to Be, Their Future, and Why Psychologists in Education Should Care
Bibliographic record
Abstract
For some three decades psychologists and educators have been working with incomplete or outdated ideas of what constitutes giftedness. Conceptual leadership in the field has moved from a definition based on IQ to expertise- and cognitive science-based definitions. Practice lags behind. Similarly, curriculum concepts are changing to foci based on thinking processes and cognitive apprenticeships, communities of learners, teachers and students as coconstructors of curriculum, and inquiry skills and knowledge as explicit parts of the objectives. Practice there also lags behind, but the shift is underway. This discussion elaborates on these two evolving phenomena, illustrated especially by studies completed and underway in the author’s laboratory group at McGill. It juxtaposes central ideas in gifted education, expertise, and inquiry-driven teaching and learning, and illustrates how these strands can come together for the benefit of all students—albeit sometimes in different ways.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.066 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".