From Individual Word Recognition to Word List and Text Reading Fluency
Bibliographic record
Abstract
This study aimed to examine (a) the developing interrelations between the efficiency of reading individually presented words (i.e., isolated word recognition speed) and the efficiency of reading multiword sequences (i.e., word list and text reading fluency), (b) whether serial digit naming, indexing the ability to process multi-item sequences, accounts for variance in word list and text reading fluency beyond isolated word recognition speed, and (c) if these patterns of relations/effects differ between two alphabetic languages varying in orthographic consistency (English and Greek). In total, 710 Greek- and English-speaking children from Grades 1, 3, and 5 completed a serial digit naming task and a set of reading tasks, including unconnected words presented individually, unconnected words presented in lists, and sentences forming a meaningful passage. Our results showed that the relation between isolated word recognition speed and both word list and text reading fluency gradually decreased across grades, irrespective of contextual processing requirements. Moreover, serial digit naming uniquely predicted both word-list and text reading fluency in Grades 3 and 5, beyond isolated word recognition speed. The same pattern of results was observed across languages. These findings challenge the notion that individual word recognition and reading fluency differ only in text-level processing requirements. Instead, an additional component of processing multi-item sequences appears to emerge by Grade 3, after a basic level of both accuracy and speed in word recognition has been achieved, offering a potential mechanism underlying the transition from dealing with words one at a time to efficient processing of word sequences.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".