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Record W2980240149 · doi:10.15173/ijsap.v3i2.3799

Partnership through co-creation: Lessons learnt at the University of Adelaide

2019· article· en· W2980240149 on OpenAlexvenueno aff
Catherine Snelling, B. R. Loveys, Sophie Karanicolas, Nathan James Schofield, William Carlson‐Jones, Joanne Weissgerber, Ruby Edmonds, Jenny Ngu

Bibliographic record

VenueInternational Journal for Students as Partners · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
FundersUniversity of Adelaide
KeywordsGeneral partnershipCreativityPedagogyLearning designBest practiceSociologyMathematics educationPsychologyEngineering ethicsPolitical scienceEngineering

Abstract

fetched live from OpenAlex

This paper describes three exemplars of practice inspired by emerging evidence that student-staff partnerships have the potential to significantly enhance many areas of higher education. Students and academics at the University of Adelaide have successfully implemented this collaborative approach across a range of learning and teaching contexts. The Design Thinking Framework, developed by the Hasso Plattner Institute of Design at Stanford University, was utilised at a faculty, program, and course level to frame each of the exemplars, due to its implicit approach to creativity, collaborative development, and achievement of solutions. The iterative nature of the framework facilitated a review cycle for continuous improvement in each Students-as-Partners’ initiative. Analysing the outcomes of each exemplar has identified common hallmarks of successful partnership, and these indicators have the potential to contribute to the growing body of evidence that defines best practice in this pedagogy

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)0.015
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.008
Scholarly communication0.0100.009
Open science0.0030.013
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.115
GPT teacher head0.574
Teacher spread0.459 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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