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Elders’ and Children’s Dialogue and Learning in a Canadian Intergenerational Organization

2022· book-chapter· en· W4417093892 on OpenAlexaffabout
Dany Boulanger

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsDialogical selfRelation (database)Interpretation (philosophy)Function (biology)Frontier

Abstract

fetched live from OpenAlex

This chapter tries to provide an understanding of how actors in intergenerational encounters amidst the school, the family, and the community generally misunderstand one another—lacking understanding on the frontier of private and public life. This paper addresses this issue, from a dialogical perspective, by analyzing the discourses of elders participating in intergenerational activities aimed at supporting children’s school homework. These activities take place in a Canadian community and intergenerational organization. I shed light on the issue of misunderstanding for learning in intergenerational programs in relation to who has access to knowledge and how mediating is realized. The analysis displays elders’ limits in mediating children’s learning, thanks to a lack of mediated communication with children’s teachers through the agenda. The interpretation underscores the function of language in deepening the understanding of elders’ discourse, mainly with regards to elders’ relation to the family.

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)0.001
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: none
Teacher disagreement score0.062
Threshold uncertainty score0.452

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0180.013
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.260
Teacher spread0.237 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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