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Record W4381937403 · doi:10.15173/ijsap.v7i1.5032

A multidisciplinary STEM and liberal arts students-as-partners project promoted the development of employability skills and embodied partnership values

2023· article· en· W4381937403 on OpenAlexvenueno aff
Louise Lexis, Brianna Julien, Birgit Loch, Mark Civitella, Brianne Keogh, M. J. Boffa, Tessa Jelley, Fiona Sawyer, Selin Ramadan, Pranita Pokhriyal, J. P. Carpenter

Bibliographic record

VenueInternational Journal for Students as Partners · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsEmployabilityUnderpinningTransformative learningGeneral partnershipCurriculumPedagogyLiberal arts educationFocus groupMultidisciplinary approachInclusion (mineral)PsychologyMedical educationSociologyHigher educationPolitical scienceEngineeringMedicineSocial science

Abstract

fetched live from OpenAlex

Few studies have explored student perceptions of participating in STEM multidisciplinary students-as-partners (SaP) projects integrated into the curriculum. We conducted content analysis on focus groups to determine partner perceptions of a STEM and liberal arts SaP project and used a mixed methods concurrent triangulation design to explore the degree to which partners thought the underpinning SaP values had been enacted. Four staff and seven students participated in the study. Perceptions of the project were aligned to four themes: outstanding student experience, development of student employability skills, a transformative change in the student-to-staff relationship, and barriers to success and enabling strategies. Qualitative and quantitative data indicated strong inclusion of the partnership values. This paper provides new insights into STEM and liberal arts SaP projects, indicating they may be well-suited to the embodiment of the underpinning SaP values, and help students prepare for the world of work.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.169
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.138
GPT teacher head0.579
Teacher spread0.441 · 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
Published2023
Admission routes1
Has abstractyes

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