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Record W3200594160 · doi:10.20343/teachlearninqu.9.2.9

From Assistants to Partners

2021· article· en· W3200594160 on OpenAlexafffund
Jennifer Lock, Carol Johnson, Laurie Hill, Christopher Ostrowski, Luciano da Rosa dos Santos

Bibliographic record

VenueTeaching & Learning Inquiry The ISSOTL Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsMount Royal UniversityUniversity of Calgary
FundersUniversity of MelbourneMount Royal UniversityQueen's UniversityUniversity of CambridgeSt Mary's University
KeywordsGeneral partnershipScholarship of Teaching and LearningScholarshipGraduate studentsSociologyPedagogyMedical educationPolitical scienceTeaching methodTeaching and learning centerMedicine

Abstract

fetched live from OpenAlex

Student-faculty partnerships are a growing practice in scholarship of teaching & learning (SoTL) projects. They can foster greater student engagement in higher education and help advance teaching & learning experiences. For graduate students, in particular those pursuing academic careers, such partnerships can offer opportunities for development of their professional identities as emerging SoTL scholars. In this article, we expand upon previous theorizations of partnerships to include the unique attributes of graduate student partnerships, such as in terms of longer timeframes, increased complexity, and long-term goals. Drawing on a two-year SoTL study, we present a three-layer framework characterizing key attributes for a successful graduate student-faculty partnership: 1) individual attributes in a partnership, 2) collective attributes for a partnership, and 3) outcomes of a partnership. The framework is grounded in literature and illustrative examples from our experiences as graduate students and faculty members working together in partnership with a SoTL project. This framework offers a structured mechanism to inform, create, and enhance the capacity of student-faculty partnerships in SoTL research.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.121
GPT teacher head0.452
Teacher spread0.331 · 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.

Study designNot applicable
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

Citations7
Published2021
Admission routes2
Has abstractyes

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