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Record W3213946984 · doi:10.31468/dwr.873

PhD Students Learning the Process of Academic Writing: The Role of the Rhetorical Rectangle

2021· article· en· W3213946984 on OpenAlex
Beverly FitzPatrick, Mike Chong, James Tuff, Sana Jamil, Khalid Al Hariri, Taylor Stocks, Christopher Cumby

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueDiscourse and Writing/Rédactologie · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRhetorical questionEthosPathosPedagogyNarrativeFeelingWriting processPsychologyAcademic writingProfessional writingKairosRhetoricMathematics educationSociologyLiteratureArtSocial psychologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

PhD students are enculturated into scholarly writing through relationships with their supervisors and other faculty. As part of a doctoral writing group, we explored students’ experiences that affected their writing, both cognitively and affectively, and how these experiences made them feel about themselves as academic writers. Six first and second year doctoral students participated in formal group discussions, using Edward de Bono’s (1985/1992) Six Thinking Hats to guide the discussions. In addition, the students wrote personal narratives about their writing experiences. Data were analyzed according to the rhetorical rectangle of logos, ethos, pathos, and kairos. Analysis revealed that students were having struggles with their identities as academic writers, not feeling as confident as they had before their programs, and questioning some of the pedagogy of teaching academic writing.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptScholarly communication
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.312
GPT teacher head0.575
Teacher spread0.264 · 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