Bibliographic record
Abstract
Wide socio-demographic disparities exist between students identified as gifted and their peers (De Valenzuela, Copeland, Qi, & Park, 2006; Leonardo & Broderick, 2011). In this paper, we examine the intersectional construction of giftedness and the academic achievement of students identified as gifted. Using data from the Toronto District School Board (TDSB), the largest and one of the most diverse public education systems in Canada, we consider racial, class, and gender characteristics of students identified as gifted in comparison to those who have very high achievement. Results demonstrated that there was almost no relationship between students identified as gifted and students who had very high achievement (Pearson’s correlation of 0.18). White, male students whose parents had high occupation statuses had the highest probability of being identified as gifted. Female students were more likely to be high achievers. Compared to White students, it was only East Asian students who were more likely to be identified as gifted; yet South, Southeast and East Asian students were more likely to be very high achievers. Parental occupation was strongly related to both giftedness and very high achievement. Results point to the socially constructed nature of giftedness and challenge its usage in defining and organizing students in schools.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".