Aligning Elements of the Identification Process: Implications for Hidden Exceptionalities
Bibliographic record
Abstract
Students with learning disabilities and other high-incidence hidden exceptionalities (HIHE) can struggle with secondary difficulties including low confidence in their abilities (e.g., Klassen, 2010) along with primary difficulties related to their exceptionality. The purpose of this paper is to highlight the importance of the perspective of individuals with hidden exceptionalities with regard to maladaptive beliefs they can form. Using learning disabilities as a platform for discussing HIHE, we unpack two types of maladaptive beliefs—related to generalizations and mindset—and suggest adaptive alternative beliefs that can promote adaptive behaviour. The focus on maladaptive beliefs draws attention to the identification process, and specifically the types of variables that can influence how well aligned parts of the process are for particular students. We present a framework that contextualizes individual beliefs within the identification process, and what other variables determine alignment of parts within the process. The framework can be used to support educators through the identification process, as well as researchers in providing direction for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".