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Record W2979356167 · doi:10.15173/ijsap.v3i2.3714

Developing a collaborative book project on higher education pedagogy: The institutional, organizational, and community identity dimensions of student-staff partnerships

2019· article· en· W2979356167 on OpenAlexvenueno aff
Lauren Clark, Agathe Ribéreau‐Gayon, Mina Sotiriou, Alex Standen, Joe Thorogood, Vincent C. H. Tong

Bibliographic record

VenueInternational Journal for Students as Partners · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipPedagogyIdentity (music)SociologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

This case study presents an ambitious student-staff partnership project at University College London (UCL) to publish a collaborative book on higher education pedagogy. Over two-and-a-half years, a total of 86 students and staff contributed to the project, which sought to provide educators with a new type of scholarly material under the unifying theme of connecting research and teaching. Multiple layers of student-staff partnership were interwoven throughout the project; this case study contextualizes these layers against three dimensions: institutional, organizational, and community identity. Central to the project was our distinctive approach to engaging with Graduate Teaching Assistants (GTAs) and their crucial role in bringing the three dimensions together. As such, the project represents a model of enhanced student-staff partnership that has the capacity to empower students and break down educational silos to form new, multi-specialty learning communities.

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)0.016
metaresearch head score (Gemma)0.017
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 score0.016
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.008
Scholarly communication0.0080.006
Open science0.0020.011
Research integrity0.0030.003
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 head0.175
GPT teacher head0.578
Teacher spread0.403 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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