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Record W3094936672 · doi:10.15173/ijsap.v4i2.4289

Partners, not peers: Defining boundaries and expectations in student partnerships

2020· article· en· W3094936672 on OpenAlexvenueno aff
Holly Pelnar, Grace Reyes, Kiran Sehgal, Leslie Cameron

Bibliographic record

VenueInternational Journal for Students as Partners · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

In this reflection, one current student (Holly Pelnar), two former students (Grace Reyes and Kiran Sehgal), and one faculty member (Leslie Cameron) from Carthage College, a small liberal arts college in the United States, reflect on their experiences in student-faculty partnerships in the scholarship of teaching and learning (SoTL).From the faculty perspective, one of the most compelling aspects of conducting research in SoTL with undergraduate students as partners (SaP) is that the focus of the research is the students and their learning.Crucially, students also offer a unique perspective as they have relevant lived experience.From the student perspective, working as a student partner is motivating because we are working on topics that are relevant to our own learning and we have an opportunity to work closely with faculty in a unique research environment.In this essay we have collaboratively reflected on our experiences working together and identified challenging aspects of the partnership that we think could be improved to enhance the experience.

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)0.033
metaresearch head score (Gemma)0.071
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 score0.033
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.071
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0200.028
Scholarly communication0.0320.026
Open science0.0030.035
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.001

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 head0.192
GPT teacher head0.576
Teacher spread0.384 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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