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Record W2152602048 · doi:10.1002/sce.20266

Learning to read scientific text: Do elementary school commercial reading programs help?

2008· article· en· W2152602048 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Education · 2008
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReading (process)Variety (cybernetics)Mathematics educationSet (abstract data type)Science educationComputer scienceScience learningScientific literacyPsychologyPedagogyLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

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 Ed 92: 765–798, 2008

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.058
GPT teacher head0.380
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it