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Record W4381891877 · doi:10.4324/9781003443926-2

Decentering the United States in International Service-Learning

2023· book-chapter· en· W4381891877 on OpenAlexvenueno aff
Amanda L. Espenschied-Reilly, Susan V. Iverson

Bibliographic record

VenueCrossing boundaries · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsService-learningService (business)Political scienceBusinessLawMarketing

Abstract

fetched live from OpenAlex

Service-learning emerged in the United States as a grassroots movement in the 1960s and 1970s and more recently has been gaining recognition in many regions of the world. As “Americanized” conceptions of service-learning circulate internationally, educators need to develop clarity about which aspects of service-learning can be adapted to diverse social, cultural, and economic contexts so as not to repeat the mistake of “exporting Western ideas and practice methodologies which may or may not be relevant”. This chapter presents an investigation that was an exploratory pilot interview study designed to compare service-learning in Ireland and in the United States in order to discern the ways in which culture and social context shape practitioners’ perceptions and practices. Service-learning in the United States and Ireland has very different histories. Yet, respondents shared similar challenges related to institutionalization, whether they were involved in a nearly 25-year project in the United States or an initiative in existence for less than a decade in Ireland.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.042

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 studies0.0050.005
Scholarly communication0.0090.007
Open science0.0000.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.002

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 head0.061
GPT teacher head0.324
Teacher spread0.263 · 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
Published2023
Admission routes1
Has abstractyes

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