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Record W3149071235 · doi:10.29173/iasl7836

Supporting the Personal and Interpersonal Skills of Global Citizens Through Fiction

2021· article· en· W3149071235 on OpenAlexvenueno aff
Barbara Reid, Siobhan Roulston

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsInterpersonal communicationPresentation (obstetrics)Variety (cybernetics)PsychologyCoping (psychology)Social skillsInterpersonal relationshipPeople skillsPedagogyMedical educationSkills managementSocial psychologyComputer scienceMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

How can Teacher-Librarians collaborate with teachers, school counselors and parents to support and teach values, coping skills and the management of interpersonal relationships? What tools can Teacher-Librarians use to inform the school community about the range of resources available? This practical presentation introduces a variety of texts that can be used with Primary and Middle school students and suggests how they might fit into a school setting. The selected texts will be predominantly picture books written in English and sourced from a range of countries and cultures. Teacher-Librarians are often approached to suggest books that will assist the school community to develop confident, empathetic global citizens. They must ensure that these books are easily accessible. They should promote them and suggest how and when they may be used with individuals and/or in a classroom setting. Participants will be invited to add their own suggestions to the list of books provided.

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)0.004
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.003

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 head0.024
GPT teacher head0.349
Teacher spread0.324 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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