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Record W2589309202 · doi:10.1186/s40554-017-0041-9

Disciplinary variations in academic promotional writing: the case of statements of purpose

2017· article· en· W2589309202 on OpenAlexafffund
Sibo Chen

Bibliographic record

VenueFunctional Linguistics · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsDisciplineRhetorical questionPersuasionAcademic writingDiscourse communityGenre analysisEnglish for academic purposesApplied linguisticsMainlandSociologyScientific writingMainland ChinaLinguisticsPedagogyPolitical scienceSocial scienceChina

Abstract

fetched live from OpenAlex

Abstract This paper explores disciplinary variations in academic promotional writing via a comparative analysis of statements of purpose (SoPs) written for different disciplines. A total of 100 SoPs written by English as an additional language (EAL) applicants from mainland China were collected, which were drawn from five academic disciplines: business, engineering, humanities, science and social science. Following a corpus-driven research design, these SoP samples were analyzed in terms of their lexico-grammatical and rhetorical features. The data analysis suggests that although on the surface these SoP samples share similarities in lexico-grammatical and rhetorical features, they are quite different in terms of their preferred persuasion strategies. While SoPs written for engineering and science primarily base their self-promotional arguments upon the applicants’ previous research experiences and future research prospects, those written for business, humanities and social science tend to focus on how the applicants’ unique Chinese socio-cultural backgrounds would contribute to their desired programs. The above finding sheds light upon how academic genres are invariably embedded in disciplinary practices, with each discipline having its own communicative purposes, discourse community members, academic expectations and disciplinary constraints.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.100
GPT teacher head0.383
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2017
Admission routes2
Has abstractyes

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