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Record W2887746835 · doi:10.1080/0309877x.2018.1483013

Promoting and/or evading change: the role of student-staff partnerships in staff teaching development

2018· article· en· W2887746835 on OpenAlexafffund
Elizabeth Marquis, Emily Power, Melanie Yin

Bibliographic record

VenueJournal of Further and Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsGeneral partnershipScholarshipTeaching staffProfessional developmentPedagogyFaculty developmentHigher educationFocus groupMedical educationPsychologyPublic relationsSociologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

While a large body of research considers factors enabling or constraining academic development in colleges and universities, comparatively little scholarship has considered the roles students might play in supporting positive change in staff teaching practices. This article explores one potential avenue by which such change might play out, considering the extent to which participation in a student-staff partnership programme supported by a central teaching and learning institute might encourage shifts in staff teaching. Drawing on data gathered via focus groups and online reflective prompts, we find that participating in pedagogical partnership can support a range of developments in staff teaching practices, though these changes might not always be pronounced or uniformly positive. Implications for future research and practice are discussed.

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)0.037
metaresearch head score (Gemma)0.061
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 score0.037
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.061
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.012
Scholarly communication0.0150.009
Open science0.0020.016
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.117
GPT teacher head0.409
Teacher spread0.292 · 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

Citations12
Published2018
Admission routes2
Has abstractyes

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