Supporting Students with High-Incidence Hidden Exceptionalities through Non-Academic Intervention
Bibliographic record
Abstract
The purpose of this paper is to emphasize the importance of the student perspective of individuals with high-incidence hidden exceptionalities (HIHE), using learning disabilities (LD) as a platform for contextualizing current issues and possible solutions. Without changing structures and processes with regards to identification and support, it is possible to find ways to address maladaptive beliefs and perspectives that students with hidden exceptionalities can form about their difficulties and identity that are rooted in inaccuracies and (generalizations) and self-imposed limitations (maladaptive mindset). We suggest adaptive alternatives to maladaptive beliefs that focus on the way students view their learning-related challenges (learned helplessness), the way they view exceptionalities and potential for growth (maladaptive mindset), and the extent to which they feel their exceptionality impacts learning (generalizations). We argue that in addition to academic interventions that focus on skill deficits specific to the student, equipping students with HIHE with adaptive beliefs about their exceptionality can empower them to thrive despite the challenges they face in school.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".