Cognitive and linguistic factors in reading acquisition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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;
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it