Bibliographic record
Abstract
Working memory’s limited capacity places significant constraints on people's ability to hold information while processing. However, skilled readers are able to effectively encode important information into long-term memory during comprehension. This chapter describes the long-term working memory theory (LT-WM), originally developed to explain how experts in various domains (including reading) enhance their working memory capacity by relying on rapid, skilled use of long-term memory. We first trace the development of the theory and the reasons it took the form it did in the mid-1990s. We explain that LT-WM was not viewed as a new form of memory, but rather as highly practiced use of long-term memory to rapidly and reliably link information together using meaningful associations, retrieval structures, and preexisting knowledge. Next, we describe how the theory accounted for many central phenomena in discourse comprehension. More recent work has proposed a form of LT-WM for syntactic processing as well, and we discuss current critiques of the original evidence advanced to support LT-WM. Finally, we describe recent studies on neural activity associated with LT-WM development in reasoning skills and language comprehension.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".