Self-perceptions of twice-exceptional students: The influence of labels and educational placement on the self-concept of post-secondary G/LD students
Bibliographic record
Abstract
Research highlights the importance of positive self-concept for children and the influence of self-concept on long-term success (Elbaum, 2002; Fong & Yuen, 2009; Rudasill, Capper, Foust, Callahan, Albaugh, 2009), yet studies have rarely focused on the self-perceptions of self-concept of students identified as gifted and with a learning disability (G/LD). Adopting a qualitative case study approach, this study explored how eight post-secondary G/LD students perceived the development of self-concept over time, and how labelling and educational placement influenced those self-perceptions. Data collection included a demographic questionnaire, a Body Biography, and a semi-structured interview that focused on the Body Biography and participants’ self-perceptions of educational placement, labels, social identity, group membership, and self-concept. Guided by the Marsh/Shavelson model of self-concept (1985) and the Social Identity Theory (1986), findings revealed that participants often perceived the gifted and LD components of the G/LD identification as separate entities; that a gifted in-group membership was more often perceived when discussing individual strengths, while an LD in-group membership was perceived when reflecting upon their weaknesses. The findings from this study support the notion that each G/LD student is unique and that identification methods and placement options continue to be a concern with respect to the development of self-concept for G/LD students.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".