On the Importance of a Cognitive Processing Perspective: An Introduction
Bibliographic record
Abstract
Children with learning problems require early intervention. If it is evidence based and implemented with integrity and intensity, it will accelerate the academic progress of many students. This is the hope and expectation of the many supporters of responsiveness-to-intervention (RTI). A minority of children, however, will not respond sufficiently to such intervention because of learning disorders like specific learning disabilities (SLD). Some RTI models do not include research-backed methods to identify these children, nor do RTI practitioners often produce the data necessary to develop individualized instruction for them. The authors suggest practitioners go beyond typical RTI assessment data documenting responsiveness/ unresponsiveness to conduct comprehensive evaluations of these most difficult-to-teach students and to include in their evaluations carefully chosen cognitive measures. This special issue presents the work of teams of researchers, which suggests that cognitive and neuropsychological assessments can provide information to further understand SLD, which in turn can guide development of promising interventions.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".