Morphological and Syntactic Awareness in Poor Comprehenders
Bibliographic record
Abstract
Poor comprehenders have intact word-reading skills but struggle specifically with understanding what they read. We investigated whether two metalinguistic skills, morphological and syntactic awareness, are specifically related to poor reading comprehension by including separate and combined measures of each. We identified poor comprehenders (n = 15) and average comprehenders (n = 15) in Grade 4 who were matched on word-reading accuracy and speed, vocabulary, nonverbal cognitive ability, and age. The two groups performed comparably on a morphological awareness task that involved both morphological and syntactic cues. However, poor comprehenders performed less well than average comprehenders on a derivational word analogy task in which there was no additional syntactic information, thus tapping only morphological awareness, and also less well on a syntactic awareness task, in which there were no morphological manipulations. Our task and participant-selection process ruled out key nonmetalinguistic sources of influence on these tasks. These findings suggest that the relationships among reading comprehension, morphological awareness, and syntactic awareness depend on the tasks used to measure the latter two. Future research needs to identify precisely in which ways these metalinguistic difficulties connect to challenges with reading comprehension.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".