Between-word processing and text-level skills contributing to fluent reading of (non)word lists and text
Bibliographic record
Abstract
Abstract Recent studies have shown that fluent reading of word lists requires additional skills beyond efficient recognition of individual words. This study examined the specific contribution of between-word processing (sequential processing efficiency, indexed by serial digit RAN) and subskills related to text-level processing (vocabulary and syntactic skills) to a wide range of reading fluency tasks, while accounting for within-word processes (i.e., those involved in phonological recoding, orthographic decoding, and sight word reading). The sample included 139 intermediate-level (Grade 3, n = 78) and more advanced (Grade 5, n = 61) readers of Dutch. Fluency measures included simple and complex lists of words and nonwords, and a complex text. Data were analyzed through hierarchical regressions and commonality analyses. The findings confirm the importance of between-word processing for fluent reading and extend evidence from simple word lists and texts to complex word lists and texts, and simple and complex lists of nonwords. The findings hold for both intermediate-level and more advanced readers and, as expected, the contribution of between-word processing increased with reading-skill level. Effects of vocabulary were generally absent, aside from a small effect on text reading fluency in Grade 3. No effects of syntactic skills were found, even in more advanced readers. The results support the idea that once efficient individual word recognition is in place, further fluency development is driven by more efficient between-word processing. The findings also confirm that vocabulary may be less prominent in processing mechanisms underlying fluent word identification in transparent orthographies, across reading levels.
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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 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".