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Record W2234525076 · doi:10.47678/cjhe.v45i4.184723

Graduate Writing Assignments Across Faculties in a Canadian University

2015· article· en· W2234525076 on OpenAlexaffvenueabout
Ling Shi, Yanning Dong

Bibliographic record

VenueCanadian Journal of Higher Education · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGraduate studentsWriting processAcademic writingMathematics educationComputer scienceEnglish for academic purposesProcess (computing)Order (exchange)PsychologyPedagogy

Abstract

fetched live from OpenAlex

This study examines 143 graduate assignments across 12 faculties or schools in a Canadian university in order to identify types of writing tasks. Based on the descriptions provided by the instructors, we identified nine types of assignments, with scholarly essay being the most common, followed by summary and response, literature review, project, review, case analysis, proposal, exam, and creative writing. Many assignments are instructor-controlled and have specific content requirements. Some are also process-oriented, providing students with teacher or peer feedback on outlines or initial drafts, suggestions for topic choices, and examples of good writing. With an overview of the types of writing tasks across campus, the study has implications for English for Academic Purposes (EAP) or graduate writing program designers, material developers, educators working within and across disciplines, and researchers interested in the types of university writing assignments in Canada.

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)0.007
metaresearch head score (Gemma)0.037
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: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.516

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.008
Science and technology studies0.0130.003
Scholarly communication0.0040.001
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.083
GPT teacher head0.318
Teacher spread0.235 · 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

Citations17
Published2015
Admission routes3
Has abstractyes

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