Age-Related Changes in Reading Comprehension: An Individual-Differences Perspective
Bibliographic record
Abstract
In this study, the authors show that Hannon and Daneman's (2001 Hannon , B. & Daneman , M. ( 2001 ). A new tool for measuring and understanding individual differences in the component processes of reading comprehension . Journal of Educational Psychology , 93 , 103 – 128 .[Crossref], [Web of Science ®] , [Google Scholar], Journal of Educational Psychology, 93, 103–128) component processes task can be used to investigate individual differences in older readers' comprehension performance, and to determine which components of comprehension are most susceptible to declines with normal aging. Results revealed that the ability to remember new text information, to make inferences about new text information, to access prior knowledge in long-term memory, and to integrate prior knowledge with new text information all accounted for a substantial proportion of variance in older adults' reading comprehension performance. Although there were age-related declines in all of these component processes, the components associated with new learning were more susceptible to age-related declines than were the components associated with accessing what already is known. The findings suggest that age-related declines in reading comprehension might be a consequence of declines in a number of component processes rather than one specific process.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".