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Record W1009843737 · doi:10.29173/slw6861

The use of self-story as a pedagogical tool in a meta-cognitive exercise to support children in understanding their material choices in the school library

2013· article· en· W1009843737 on OpenAlexvenueno aff
Linda L. Cooper

Bibliographic record

VenueSchool Libraries Worldwide · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyFeelingTask (project management)PsychologyCognitionLanguage developmentZone of proximal developmentMetacognitionMathematics educationLinguisticsDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

This paper examines the notion of self-story as a sense-making scaffold to self-knowledge via the Zone of Proximal Development in a school library setting. A teaching strategy is presented utilizing ideas from Vygotsky, Bruner, and Dervin in which children are asked to remember stories about themselves to support language development and movement towards greater self knowledge supporting their choices of material in the school library. Children may lack the vocabulary/language to describe/explain their own behavior. Since language is culturally constructed and children lack experience with culture and, thus, language, not only may they have difficulty communicating with others, but since thinking is informed by language, they may have difficulty understanding their own thoughts and feelings since they do not have the words to name them. They need to access the appropriate language/words to express these things. One strategy that may assist them in this task is the use of self-stories.

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)0.003
metaresearch head score (Gemma)0.009
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.242
GPT teacher head0.354
Teacher spread0.112 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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Same venueSchool Libraries WorldwideSame topicTeacher Education and Leadership StudiesFrench-language works237,207