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Record W220055905 · doi:10.29173/slw6967

Informational Empowerment: Using Informational Books to Connect the Library Media Center Program with Sheltered Instruction

2007· article· en· W220055905 on OpenAlexvenueno aff
Jamie Campbell Naidoo

Bibliographic record

VenueSchool Libraries Worldwide · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsSchool librarySheltered instructionActive listeningMathematics educationPedagogyEmpowermentComputer scienceSelection (genetic algorithm)StorytellingPsychologySociologyLibrary scienceLanguage educationLinguisticsComprehension approachPolitical science

Abstract

fetched live from OpenAlex

Sheltered instruction (SI) is a teaching strategy that allows the school library media specialist to collaborate in the English-as-a-second language (ESL) program to help English language learner (ELL) students integrate second-language acquisition skills with content area instruction. By aligning ESL Standards for Pre-K-12 Students with Information Power standards, a powerful collaborative effort is formed between the school library media specialist and ESL teachers. These two United States standards for education allow ESL teachers to learn selection criteria and teaching strategies for using informational trade books with ELL students while providing an opportunity far the school library media specialist to learn how better to assist ELL students with acquiring information. Read-alouds, puppetry, book talks, storytelling, author studies, and listening centers are useful approaches for incorporating the various genres of informational books into sheltered instruction.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.003
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.002

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 head0.016
GPT teacher head0.275
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2007
Admission routes1
Has abstractyes

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