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Record W4415225067 · doi:10.15173/ijsap.v9i2.6698

Embedding student voice in European alliances

2025· article· en· W4415225067 on OpenAlexvenueno aff
Nicole Messi

Bibliographic record

VenueInternational Journal for Students as Partners · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistic Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsKey (lock)Focus (optics)Grounded theoryHigher educationEmbeddingFocus groupBest practice

Abstract

fetched live from OpenAlex

This article explores how student-faculty pedagogical partnerships, widely implemented in North American institutions, can inform the development of inclusive and collaborative teaching models within European university alliances, with a focus on UNITA Universitas Montium. Grounded in the student voice movement, the study analyzes four U.S. case studies, including the SaLT program at Bryn Mawr and Haverford Colleges, through semi-structured interviews with students, faculty, and program coordinators. Guided by a qualitative, embedded case study design, the research identifies key features, benefits, challenges, and enabling conditions for successful partnerships. Findings emphasize the importance of role equality, structured training, open communication, institutional support, and recognition. The study argues that, while student-faculty partnerships are still emerging in European consortia, they hold strong potential for fostering student engagement, pedagogical innovation, and a shared academic culture. The paper concludes by offering recommendations for adapting such models within multilingual, intercultural, and geographically diverse settings like those found in the UNITA alliance.

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)0.027
metaresearch head score (Gemma)0.024
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.019
Scholarly communication0.0180.016
Open science0.0020.032
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.091
GPT teacher head0.583
Teacher spread0.492 · 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
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
Published2025
Admission routes1
Has abstractyes

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