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Record W4410495893 · doi:10.15173/ijsap.v9i1.5761

Comparing students as partners (SaP) in higher education in Australia and Japan

2025· article· en· W4410495893 on OpenAlexvenueno aff
Andrea Dodo-Balu, Fumiko Inoue

Bibliographic record

VenueInternational Journal for Students as Partners · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationGeographyPsychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

Students as partners (SaP) is an approach which aims to include students and staff in partnership in a number of settings across higher education. This article is a comparative exploration of SaP in relation to universities in Australia and Japan, through the lens of Matthews’ (2017) five propositions for SaP. Published in 2017, the propositions articulate the transformative potential of genuine, agentic engagement between staff and students in partnership. SaP is well recognized as a teaching and learning strategy in Australian higher education, yet there are changing and evolving perspectives. While the concept of SaP is less widely known in Japan, actively involving and engaging students in their own learning is an approach that is acknowledged and encouraged. Through a collaborative examination of our respective contexts, we found that the value of SaP is retained across the two cultures, along with its potential to transform and enrich both higher education systems.

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)0.009
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 score0.027
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.005
Scholarly communication0.0070.005
Open science0.0010.016
Research integrity0.0010.002
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 head0.160
GPT teacher head0.623
Teacher spread0.463 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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