Investigating Differences in Professionals’ Use of Information for Learning Disability Identification
Bibliographic record
Abstract
A learning disability (LD) can be defined as unexpected or chronic underachievement that cannot be explained by any other cognitive deficits (Swanson, Harris & Graham, 2013). LD has been said to be one of the least understood disabilities to affect school-aged population (Lyon et al., 2001). Different models may be used to identify an LD (e.g., Ability-Achievement Discrepancy and Response to Intervention (RtI) Models). Three groups of professionals (practicing psychologists, pre-service psychologists and pre-service teachers) were recruited from the Edmonton area. Participants were given three different cases and were asked to determine their confidence in both their ability to make a decision about the student needs and ability to interpret the data provided in the cases. Finally, the professional’s evaluated which case was most likely or least likely to have an LD. Pre-service psychologists were able to identify the model that combined both RtI and the ability-achievement discrepancy at a rate higher than both practicing psychologists and pre-service teachers. The pre-service teacher’s answers were dispersed among all three cases, confirming that these professionals would be no greater than chance in identification of an LD. The preliminary results of this small sample size study indicate that both pre-service psychologists and practicing psychologists found the case that combined both the ability-achievement discrepancy model and RtI model most useful in the identification of an LD.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".