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Record W4411128809 · doi:10.5539/hes.v15n3p90

Designing Reciprocal Short-Term Engagement Activities Abroad

2025· article· en· W4411128809 on OpenAlexvenueno aff
Irena Gorski, Kate Manni, Kayla M. Johnson, Khanjan Mehta

Bibliographic record

VenueHigher Education Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsStudy abroadReciprocalTerm (time)PsychologyHigher educationMathematics educationPedagogyEconomic growthEconomics

Abstract

fetched live from OpenAlex

As demand for short-term international engagement experiences continues to grow, it is essential to support faculty in designing activities that are ethical, reciprocal, feasible, and academically rigorous. This article offers practical guidance for faculty to design, plan, facilitate, and reflect on short-term engagement activities abroad—defined here as experiences lasting between four hours and three days. It outlines key steps for preparation, highlights best practices for implementation, and provides strategies to assess community involvement, align with learning outcomes, ensure academic rigor, and evaluate impact. Recognizing that many first-time faculty advisors face challenges in establishing international programs or incorporating meaningful short-term engagement within them, this article presents actionable prompts and insights to aid in the development of effective and ethically grounded activities. By addressing common pitfalls and offering a structured approach, the article aims to enhance the quality and integrity of short-term international engagements in higher education.

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)0.015
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0020.009
Research integrity0.0020.003
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 head0.070
GPT teacher head0.407
Teacher spread0.337 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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