Cognitive and linguistic factors in reading acquisition
Bibliographic record
Abstract
Models of the reading process generally describe the relations among the components of reading in skilled readers.In these models, the relations between bottom-up word recognition processes (lower order processes) and top-down comprehension processes (higher order processes) are typically described.In bottom-up models of reading, processing starts with the raw input and passes through increasingly refined analyses until the meaning of the text is grasped.In top-down models, the decisions made at higher levels of processing are used to guide choices at lower levels.Research evidence makes it clear that neither purely bottom-up nor purely top-down models can fully explain the reading process (Rayner & Pollatsek, 1989;Stanovich, 2000).An interactive model of ongoing top-down and bottom-up processes is therefore needed to imply that the reader uses both graphic and contextual information to grasp the meaning of a text (Perfetti, Landi, & Oakhill, 2005;Verhoeven & Perfetti, 2008).In the process of learning to read, children start out acquiring elementary decoding skills and learn to apply these with greater accuracy and speed.Word recognition subsequently becomes increasingly automatized by direct recognition of multi-letter units and whole words (Reitsma, 1983;Ziegler & Goswami, 2005).Automatic word recognition enables children to devote their mental resources to the meaning of text rather than to recognizing words, allowing them to use reading as a tool to acquire new concepts and information (Perfetti, 1998;
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".