Unpacking Reading Comprehension: An Examination of Text Type and Cognitive Functioning with Poor and Typically-Achieving Comprehenders
Bibliographic record
Abstract
In the present study, the researcher examined how students build comprehension with different types of text. Poor comprehenders and typically-achieving comprehenders, as determined by a standardized measure for general reading comprehension, were compared in their reading comprehension and reading strategy use across text types. The researcher also examined the influence of cognitive functioning on reading comprehension, and to what extent cognitive functions can explain the difference in reading comprehension between poor and typically-achieving comprehenders. A group of poor comprehenders ( n = 24) and typically-achieving comprehenders ( n = 38) completed measures of cognitive functioning and read narrative, expository, and graphic texts aloud before answering comprehension questions. Participants verbally reported strategy use during reading allowing the researcher to make comparisons between the two groups. Poor comprehenders used fewer cognitive strategies than their typically-achieving peers while reading narrative and expository text, and fewer evaluative strategies with graphic text. Participants
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".