Revisiting the simple view of reading
Bibliographic record
Abstract
BACKGROUND: Reading component models such as the Simple View of Reading (SVR; Gough & Tunmer, 1986; Hoover & Gough, 1990) provide a concise framework for describing the processes and skills involved when readers comprehend texts. According to the Independent Review of the Teaching of Early Reading (Rose, 2006) strong evidence for the SVR comes from Factor Analysis of datasets on different measures of reading showing dissociation between decoding skills and comprehension. To the best of our knowledge, only two such published studies exist to date. Of these, only one of is in English and this explores children between the age of 7 and 10 years. AIMS: To explore the SVR in English-speaking children aged 4 and 6 using Factor Analysis. SAMPLES: 116 4-year-olds and 116 6-year-olds in the US; 103 6-year-olds in Canada. METHODS: All children were administered a battery of decoding and comprehension related measures. RESULTS: Factor Analysis of the diverse measures undertaken independently by two research teams in different countries demonstrated that listening comprehension and decoding measures loaded as distinct factors in both samples of young English-speaking children. CONCLUSIONS: The present findings provide important support for the generality and validity of the SVR framework as a model of reading.
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.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".