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Record W3213472358 · doi:10.15173/ijsap.v5i2.4647

Learning together: A case study of a partnership to co-create assessment criteria

2021· article· en· W3213472358 on OpenAlexvenueno aff
Susan Smith, Kimiya Akhyani, Dan Axson, Andre Arnautu, Ilina Stanimirova

Bibliographic record

VenueInternational Journal for Students as Partners · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipInclusion (mineral)Process (computing)Public relationsBusinessPolitical sciencePsychologyComputer scienceSocial psychologyFinance

Abstract

fetched live from OpenAlex

This case study outlines a staff-student partnership to co-create generic assessment criteria to use in a UK business school. It highlights the potential for staff-student partnerships to create a temporary subfield, in which the established power differentials of academia are dissolved and partnership values can be established. We draw on a series of 10 semi-structured interviews with partnership participants. The values that underpin partnerships are linked to three major phases of the partnership process: establishing the partnership, partnership operation and atmosphere, and the partnership outcomes. The findings indicate that the values of authenticity, reciprocity, and inclusion are critical antecedents to establishing a successful partnership and that careful attention should be paid to establishing the partnership. The case extends our understanding of the partnership process by emphasising these antecedents. The study is multi-authored, which reflects an extension to the partnership process described in the case study.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.026
metaresearch head score (Gemma)0.048
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 score0.031
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0310.014
Scholarly communication0.0100.009
Open science0.0050.017
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0060.002

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 head0.145
GPT teacher head0.641
Teacher spread0.496 · 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

Labeled directly by 3 models reading the full record.

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

Citations13
Published2021
Admission routes1
Has abstractyes

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