On the Importance of a Cognitive Processing Perspective: An Introduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it