Coarse or fine? Grain size and morpho-orthographic segmentation in struggling readers.
Bibliographic record
Abstract
Morpho-orthographic segmentation, rapid parsing of complex written words into their morphological components, is a potential source of difference in word recognition between struggling and typical readers. Although typical readers use morpho-semantic representations and morpho-orthographic segmentation in processing morphologically complex words, struggling readers typically rely on morpho-semantic processes involving coarse-grained processing of whole-word units rather than morpho-orthographic segmentation involving fine-grained letter processing. We tested this limitation in struggling readers, examining reading-ability differences among chronological-age, reading-age, and adult groups in morpho-orthographic segmentation in a primed lexical decision task. We transposed letter order across the morphological boundary of complex-word primes, focusing on disruption in priming effects of morphological and pseudo-orthographic primes that involved only orthographic overlap with target words. Morpho-semantic (coarse-grained) processing in Grade 2 typical readers was indicated by no moderation of priming effects by suffix types and letter transposition. By Grade 6, evidence of emerging fine grained analysis was found in both groups, with clear evidence of both coarse and fine grained analysis in adults. Grade 6 struggling readers showed comparable patterns of coarse and fine grained analysis as Grade 6 typical readers. Although they experienced generalized priming effects, struggling readers did experience response time disruption with transposed primes, indicating that they, like Grade 6 typical readers, adopt fine-grained processing perhaps as a precursor of emerging morpho-orthographic segmentation.
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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 | 0.003 |
| 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 | 0.000 |
| 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".