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Record W4410189580 · doi:10.1353/vpr.2024.a959854

Periodicals, Undergraduate Research, and Disciplinary Futures: The Promise of Course-Embedded Undergraduate Research Experiences (CUREs)

2024· article· en· W4410189580 on OpenAlexvenueno aff
Iain Crawford

Bibliographic record

VenueVictorian periodicals review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsCourse (navigation)Futures contractDisciplineUndergraduate researchEngineering ethicsMedical educationMathematics educationPsychologySociologyEngineeringMedicineBusinessSocial scienceAerospace engineering

Abstract

fetched live from OpenAlex

Abstract: This article argues that periodical studies offers extraordinary potential to enrich student learning through creating collaborative research opportunities and making these integral to course pedagogy. After describing how undergraduate research has become recognized as an established best practice in higher education, the article notes that, even though English has lagged behind other disciplines in adopting it, periodicals are especially well-suited sites for student research. In particular, they are readily adoptable for course-embedded undergraduate research experiences (CUREs) that can engage undergraduates both in and beyond the major, as the description of a Victorian literature course taught at the University of Delaware demonstrates. The article concludes by arguing that creating such experiences is ever more important for demonstrating the value of English studies at a time of declining enrollments and when the discipline is facing growing threats from beyond the academy.

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)0.036
metaresearch head score (Gemma)0.057
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.008
Scholarly communication0.0090.006
Open science0.0020.008
Research integrity0.0010.002
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 head0.231
GPT teacher head0.548
Teacher spread0.317 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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