Learning to read scientific text: Do elementary school commercial reading programs help?
Bibliographic record
Abstract
Abstract This paper describes a comprehensive set of studies designed to assess the potential for commercial reading programs to teach reading in science. Specific questions focus on the proportion of selections in the programs that contain science and the amount of science that is in those selections, on the genres in which the science is portrayed, on the areas and topics of science covered, on the accuracy of the scientific content, on the text features used to communicate the science, and on the instructional strategies and assessment techniques recommended. The findings show that commercial reading programs have changed substantially from the days when they were dominated by literary texts and contained hardly any science. Now, there is a variety of genres and scientific content in about one fifth of the selections. The content is also generally accurate. So, there is considerable potential offered by these programs for teaching children to read science. Unfortunately, the findings also show that the recommended instructional strategies and assessment techniques do little to capitalize upon this potential. In particular, the findings demonstrate that, although most of the science is cast in the expository genre, most of the recommended instruction and assessment is more appropriate to the literary genres. © 2008 Wiley Periodicals, Inc.Sci Ed92:765–798, 2008
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".