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Record W2103198653 · doi:10.55016/ojs/ajer.v55i4.55338

Assessment Techniques Corresponding to Scientific Texts in Commercial Reading Programs: Do They Promote Scientific Literacy?

2010· article· en· W2103198653 on OpenAlexaffvenueabout
Linda M. Phillips, Stephen P. Norris, Martha L. Smith, Jodi Buker, Chandra Kasper

Bibliographic record

VenueAlberta Journal of Educational Research · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSummative assessmentFormative assessmentScientific literacyLiteracyReading (process)PsychologyVariety (cybernetics)Mathematics educationQuality (philosophy)Science educationMedical educationPedagogyComputer sciencePolitical scienceMedicine

Abstract

fetched live from OpenAlex

This research is part of a larger study of commercial reading programs used in Canada in grades 1-6. The specific purposes of the results reported here were to identify and quantify the assessment techniques suggested for the selections that contain scientific content, to show how the assessments differ by grade, to evaluate the nature and quality of the assessments, and to examine the extent to which the assessments help foster scientific literacy. It was found that the assessments occurred in six major forms and employed about a dozen assessment tools that engage students in nearly 20 tasks. Such variety is endorsed in both literacy and science education position statements. The assessments showed some weak trends by grade, but primarily left the purpose of the assessments to teachers’ judgment. The consequence is that teachers probably will choose the assessments for formative rather than summative evaluation, an approach also endorsed by literacy and science education policy statements. Hardly any of the assessments focused on the specificities of learning to read texts that are scientific such as interpreting descriptions of methods and research findings and thus had limited use in promoting this particular aspect of scientific literacy.

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 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)0.022
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.131
GPT teacher head0.543
Teacher spread0.412 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2010
Admission routes3
Has abstractyes

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