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Record W4252119974 · doi:10.31468/cjsdwr.825

Graduate Transitions

2020· article· en· W4252119974 on OpenAlexvenueaboutno aff
Jordan Stouck, Lori Walter

Bibliographic record

VenueCanadian Journal for Studies in Discourse and Writing/Rédactologie · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupGraduate studentsExploratory researchPublicationQualitative researchInterrogationPsychologyPedagogyInformation literacyMedical educationSociologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

This exploratory study researches the experiences of Canadian graduate students as they pursue writing tasks for their degree. It also explores the supports currently utilized by such students and the need for additional supports. The research uses a case study design based on qualitative focus group interviews to provide detailed information regarding graduate students’ perceived experiences with their academic writing tasks and available supports. The approach is informed by academic literacy theory. Graduate students who participated in this study identified a transition in voice, increased pressure to publish and professionalize, and misalignments between their own and supervisory and institutional expectations, which resulted in some interrogation of institutional norms. They utilized Writing Centre, online and supervisory supports, but called for additional ongoing and peer support. The study has implications for the development of new, collaborative and peer-based writing supports, as well as identifying future research areas related to interdisciplinary degrees.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.278
Threshold uncertainty score0.554

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0180.004
Scholarly communication0.0060.003
Open science0.0030.012
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0380.008

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 head0.747
GPT teacher head0.636
Teacher spread0.111 · 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
Published2020
Admission routes2
Has abstractyes

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