Beyond Word Recognition: The Role of Efficient Sequential Processing in Word- and Text-Reading Fluency Development
Bibliographic record
Abstract
Purpose Previous studies examining the inter-relations between serial and discrete naming with reading have found that the ability to efficiently process multiple items presented in a sequence (indexed by serial naming) is a unique predictor of word- and text-reading fluency. However, conclusions have been tempered by the concurrent nature of the available data and the uniformly low demands of the materials (words and texts). Here we go beyond previous studies by using more varied materials to examine the relations of serial and discrete naming with the discrete reading of words and the serial reading of word lists and connected text over time.Method Two hundred and eight English-speaking Canadian children (51% female, Mage = 7.2 years) were followed from Grade 2 to Grade 5 and were assessed on serial and discrete digit naming and serial and discrete word reading at both measurement points.Results Strong associations between discrete naming and discrete reading already from Grade 2 indicated that short and high-frequency words were processed in parallel early in development. By Grade 5, when word recognition was presumably automatized, serial naming accounted for unique variance in serial reading of word lists and connected texts after controlling for discrete word reading. More importantly, Latent Change Score modeling indicated that serial naming was the main predictor of growth in serial reading from Grade 2 to Grade 5.Conclusion These findings suggest that, beyond individual word recognition, reading fluency development also requires efficient processing of multiple items presented in serial format (termed “cascaded processing”).
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".