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Record W4382767134 · doi:10.15173/ijsap.v7i1.5122

A student-staff partnership conducting research in higher education: An analysis of student and staff reflections

2023· article· en· W4382767134 on OpenAlexvenueno aff
Helen Payne, James Cantwell, Richard Bristow

Bibliographic record

VenueInternational Journal for Students as Partners · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipMental healthContext (archaeology)PsychologyExploratory researchMedical educationQualitative researchEmpowermentPedagogyHigher educationPrincipal (computer security)MedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

This article reports on an analysis of reflections by students and staff following a student-staff partnership which conducted a qualitative exploratory inquiry case study on student mental health in higher education. Following completion of the inquiry, two engaged students and the principal investigator reflected on their experience of the partnership. The analysis resulted in the following categories: a) benefits, b) support for learning, c) motivations, d) impact, e) outputs, and f) limitations. Students learned research skills, enhancements for learning/career and empowerment. Staff experienced an eased ability to conduct research and the rewards of seeing students develop new skills. It is recommended stakeholders in higher education continue to invest in student-staff partnerships in the context of research studies and mental health inquiry to foster opportunities for positive learning outcomes for students, staff, and institutions.

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)0.054
metaresearch head score (Gemma)0.118
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 score0.054
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.118
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0130.011
Scholarly communication0.0120.005
Open science0.0030.016
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0010.001

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 head0.549
GPT teacher head0.699
Teacher spread0.150 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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