Language used in school psychological evaluation reports as predictors of SLD identification within a response to intervention model.
Bibliographic record
Abstract
Despite decades of research, much is still unknown regarding how specific learning disability (SLD) identification decisions are made, particularly how language related to sociodemographic and psychosocial factors may impact decision-making. This study employed the Linguistic Inquiry and Word Count (LIWC) method to examine the language used in school psychological reports to better understand how sociodemographic (i.e., race, socioeconomic background, and gender) and psychosocial factors (e.g., positive and negative emotion, student effort, and student social processes) related to SLD identification within a Response to Intervention (RtI) identification method. The reports of students identified as SLD contained significantly more achievement-related language (e.g., hardworking, motivated, exerting effort) compared to students who were not identified as SLD, and achievement-related language was associated with SLD identification above and beyond RtI evaluation data (i.e., academic achievement and slope). Implications for research and practice are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".