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Record W1574888418 · doi:10.20360/g2d01k

Dialogic Encounters with Early Readers through Mediated Think-Alouds: Constructing the Transactional Zone

2012· article· en· W1574888418 on OpenAlexaffvenue
Christian W. Chun, Eunice Eunhee Jang

Bibliographic record

VenueLanguage and Literacy · 2012
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsDialogicThink aloud protocolPsychologyDialogical selfReading (process)Sociocultural evolutionReading comprehensionInterviewMeaning (existential)ComprehensionLinguisticsPedagogySocial psychologyComputer scienceSociology

Abstract

fetched live from OpenAlex

In this article, we explore the processes in which four young readers make meanings through dialogic encounters with an interviewer utilizing the think-aloud as a mediator. We believe that these mediated think-alouds act as both a mediating tool to help effect change in cognition through what Swain (2006b) and others have called “languaging” (the process of verbalizing one’s thoughts aloud either by oneself or with others), and a research elicitation tool which in its nominal function analyzing how students talk about texts serves to implicate the interviewer/researcher in the process of co-constructing new texts with the reader as the source of meaning in the reading passages. In soliciting information about these four readers’ problem-solving strategies on reading comprehension questions, the interviewer employed a dialogical and sociocultural model (Bakhtin, 1981; Vygotsky, 1978, 1986) that framed the interactions as a co-constructed reading event (Maybin & Moss, 1993). Using a Vygotskian sociocultural perspective, we analyze selected extracts of verbal accounts of these elementary school students who were assigned a reading task in the attempt to answer the question: What particular meanings emerged and how were they constructed by the students and the interviewer in their generating of next texts in the process of reading?

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.488
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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.

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

Citations1
Published2012
Admission routes2
Has abstractyes

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