Commercial Reading Programs: What’s Replacing Narrative?
Bibliographic record
Abstract
There has been a widespread appeal for the inclusion of more informational text in elementary reading programs and instruction. This appeal is motivated by claims that children’s early reading material is dominated by narrative texts and by recognition of the importance of learning to read other types of text. Commercial reading programs provide a significant source of material used in reading instruction, yet there is little empirical research on the proportion of various text types within these programs. We conducted a systematic analysis of the types of text contained in three of the most widely used commercial reading programs in Grades 1 to 6 in Canada. A comparison of our results to those of previous studies confirmed that current programs contain less narrative than their predecessors. In order to determine whether informational text is replacing the gap created by the reduced amount of narrative, we paid particular attention to the presence of expository texts and other text types with informational qualities. BACKGROUND Commercial reading programs provided the dominant materials used for reading instruction in North American elementary classrooms throughout most of the 20th century (Dole & Osborn, 2003; Smith, Phillips, Leithead, & Norris, 2004). In spite of the literature-based movement of the
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".