Mythology of gifted: A case study on the impact of labeling on self-knowledge
Bibliographic record
Abstract
As there is debate over whether students within gifted education programs realize benefits from their educational labels (Berlin 2009, Moulton et al 1998), we aimed to explore the formation of self-knowledge in light of a gifted label and explore some positive social and academic adjustments contingent on this self-knowledge. Most of the participants with a single label, “gifted”, (1) spoke of a social mythology that led to pressure to perform and internal dissonance around “being gifted”. Of note, there was a marked difference in experience for those who had concomitant special learning needs (SLN) labels. Those students articulated a strong sense of self-knowledge with regards to their SLN label and spoke of direct, explicit supports, accommodations and therapies offered to them by supportive adults with specialized knowledge which contributed to positive behavioural, social-emotional, and learning adaptations based on their SLN label. We propose that several pedagogical changes need to be in place in order to help students build a stronger sense of self-knowledge. For example, specialized training in gifted education and inclusive teaching practices will help educators and parents understand the special learning needs of this population, which will aid in breaking down the myths surrounding the gifted label.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".