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Record W4390574232 · doi:10.53967/cje-rce.6457

Book Review: Students Mentoring Students in K-8 Classrooms: Creating a learning community where children communicate, collaborate, and succeed

2024· article· en· W4390574232 on OpenAlexaffvenue
Justin Patrick

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyMathematics educationPedagogyLearning community

Abstract

fetched live from OpenAlex

Diane Vetter's (2023) Students Mentoring Students in K-8 Classrooms: Creating a learning community where children communicate, collaborate, and succeed is a guide for teachers to develop pedagogical approaches and classroom environments that help empower elementary students to support each other throughout the learning process.Relevant student development and related literature such as the theories of Jean Piaget (1959) are operationalized into tangible instructions for classroom activities that are further enriched by autoethnographical anecdotes from Vetter's own experience as an elementary school teacher (pp.14-19).This book is a useful resource for K-8 educators looking to promote student collaboration and leadership in their classrooms.It also has value for education researchers looking to understand the first steps in students' leadership skill development as well as capacity building for collective decision-making and action.The book contains five chapters along with a brief introduction and conclusion.The introduction illustrates Vetter's experience teaching grade two students about the tragic events on September 11, 2001, providing a heartfelt example of the potential for Book Review: Patrick xviii

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)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.007
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0210.012

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 head0.029
GPT teacher head0.359
Teacher spread0.330 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes2
Has abstractyes

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