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Record W3080286566 · doi:10.1080/07294360.2020.1806789

‘It’s a little complicated for me’: faculty social location and experiences of pedagogical partnership

2020· article· en· W3080286566 on OpenAlexafffund
Elizabeth Marquis, Rachel Guitman, Elaina Nguyen, Cherie Woolmer

Bibliographic record

VenueHigher Education Research & Development · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsGeneral partnershipInclusion (mineral)PerceptionEquity (law)Public relationsPower (physics)Higher educationDiversity (politics)SociologyPedagogyDimension (graph theory)PsychologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Pedagogical partnership is increasingly recognized as a practice with the potential to contribute to more just and equitable post-secondary education institutions. Previous studies have reported on the ways specific institutional programs attend to issues of equity, particularly considering if and how they involve students from marginalized groups. Similarly, others have argued for the scaling-up of partnership initiatives as a way to ensure greater inclusion and diversity amongst the staff and students who participate. Whilst the literature on this dimension of pedagogical partnerships grows, further attention to the ways in which faculty social locations influence and intersect with perceptions and experiences of partnerships is merited. Our study contributes to the literature exploring this issue. Our findings show a complex range of ways in which participants understand their social locations and the ways these influence their experiences of partnership, demonstrating the need for sophisticated conceptualizations of power and risk that attend to individuals’ positions within and outside of partnership spaces.

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)0.013
metaresearch head score (Gemma)0.025
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 score0.024
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0240.030
Scholarly communication0.0150.016
Open science0.0020.022
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.539
GPT teacher head0.556
Teacher spread0.017 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

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