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Record W4395693631 · doi:10.15402/esj.v10i1.70862

Students as Engaged Partners in Directed Research Courses

2024· article· en· W4395693631 on OpenAlexafffundvenueabout
Jodi Benenson, Skylar Johnson

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsUniversité LavalUniversité de MontréalUniversity of SaskatchewanUniversité du Québec à Montréal
FundersUniversity of Ottawa
KeywordsMathematics educationPsychologyMedical educationPedagogyComputer scienceMedicine

Abstract

fetched live from OpenAlex

This report from the field reflects on the authors’ experiences in a directed research course on the topic of youth civic engagement in Canada. A literature review was co-written as part of a directed research course where the instructor was a visiting professor from the United States and the student was an undergraduate student in Canada. The content of this report was gathered during various stages of the directed research course and is informed by literature focused on students as engaged partners in teaching and learning in higher education. Specifically, we reflect on the ways viewing students as engaged partners can leverage their knowledge and lived experiences when engaging in directed research courses, especially when the student and faculty member may be coming from different countries in North America. In addition, we reflect on how designing a directed research course that views students as engaged partners can provide a rich ground for the redistribution of power in higher education and strengthen the quality of research through the co-creation of new knowledge and ideas.

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)0.957
metaresearch head score (Gemma)0.818
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9570.818
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.6120.001
Scholarly communication0.0070.002
Open science0.0020.001
Research integrity0.0000.832
Insufficient payload (model declined to judge)0.0010.000

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 head0.302
GPT teacher head0.561
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations0
Published2024
Admission routes4
Has abstractyes

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