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Record W2980077645 · doi:10.4324/9780429027840-4

Listening to Junk

2019· book-chapter· en· W2980077645 on OpenAlexaboutno aff
Stephanie Bartlett, Robert Jean LeBlanc

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
Fundersnot available
KeywordsActive listeningPsychologyCommunication

Abstract

fetched live from OpenAlex

This chapter outlines the research relationship between Stephanie Bartlett, a Calgary-based teacher, and Robert LeBlanc, a Lethbridge-based teacher educator. It focuses on the core concept in this project: the sensorial assemblage, an arrangement of materials, people, and histories that is about information and how we feel and affectively respond to that arrangement. The chapter describes how Stephanie worked with her students to create their own sensorial assemblage out of junk, a public art project for their schoolyard that drew on sight, sound, and touch. Stephanie’s work with Project Engage offered insight into the possibilities that arise from linking school and community through learning experiences. The purpose of using the sensorial assemblage of community engagement and junk de-centres the traditional role of the teacher and demonstrates how deeply social justice and place-based learning are embedded in the foundational fabric of the project, as well as the diverse entry points for both students and also teachers new to this type of pedagogy.

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)0.001
metaresearch head score (Gemma)0.003
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: Other
Teacher disagreement score0.051
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0510.014

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 head0.045
GPT teacher head0.306
Teacher spread0.261 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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