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Record W4285464864 · doi:10.32920/ryerson.14638449.v1

Using Teachers’ Volunteer Experiences in the Dominican Republic to Develop Social Responsibility in Canadian Middle-School Students: An ‘Authors in the Classroom’ Approach

2021· preprint· en· W4285464864 on OpenAlexaffabout
Judith K. Bernhard, Lisa Evans, Yohannys Marmolejo, Teresa Cosentino

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPedagogyLiteracyPerceptionIntervention (counseling)Action (physics)PsychologyIdentity (music)

Abstract

fetched live from OpenAlex

This study evaluated the potential utility of teachers’ volunteer service-learning experiences abroad to change the perceptions and actions of North American students toward cultural others. A team of Canadian teachers working with local children in the Dominican Republic used the literacy intervention Authors in the Classroom program to guide these children in authoring identity texts about themselves and their families. These multi-layered texts are discussed with emphasis on the children’s understanding of their social situation. The teachers later shared these texts with a group of Canadian Grade 8 students and had them produce their own texts. The Canadian students showed a range of depth in their understanding of the lives of impoverished children, as well as a range of responses toward action for social justice.

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)0.005
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.407

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0270.012
Scholarly communication0.0070.002
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.117
GPT teacher head0.382
Teacher spread0.264 · 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
Published2021
Admission routes2
Has abstractyes

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