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Record W4415225339 · doi:10.15173/ijsap.v9i2.6674

Accessibility, authenticity, and agility

2025· article· en· W4415225339 on OpenAlexvenueno aff
Michael Priestley, Hannah Rachael Slack, Vee Okobia, A. S. V. MacKenzie, Rachel Shemwell Rostron, Carys Lynn Hoggan, Nathan Damascus, Xinyi Zhan, Ayesha Memon, Nicola Byrom

Bibliographic record

VenueInternational Journal for Students as Partners · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsParticipatory action researchCitizen journalismBest practiceStudent engagementPostgraduate researchStrengths and weaknessesAuthentic learning

Abstract

fetched live from OpenAlex

Student loneliness is a prevalent challenge across universities around the globe. Epistemological, ethical, and efficacious challenges characterise contemporary research on student loneliness. To address this, our team of 11 staff researchers implemented a students-as-partners initiative with 16 students from 12 universities across the United Kingdom (UK). This student research team were responsible for co-producing the design, delivery, and dissemination of a research project to understand student belonging and loneliness. Adopting a participatory evaluation approach co-authored with seven members of the student research team, this paper critically examines strengths and limitations in our team’s approach to addressing common challenges in student-as-partners activities, namely engaging underrepresented groups, fostering meaningful and authentic engagement, and navigating sensitive topics. In doing so, we elicit good practice for inclusive, accessible, and impactful research with students as partners.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.563
Teacher spread0.528 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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