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Record W2583207413 · doi:10.5539/elt.v10n3p1

Evaluation in Moves: An Integrated Analysis of Chinese MA Thesis Literature Reviews

2017· article· en· W2583207413 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
FundersGuangdong Office of Philosophy and Social Science
KeywordsArgument (complex analysis)Rhetorical questionPsychologyEnglish for academic purposesAppraisal theoryChinaLinguisticsAcademic writingMathematics educationSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

The ultimate communicative purpose of literature reviews is to convince the reader of the worthiness of the writer’s research, which is realized stage by stage and evaluation plays an important role in achieving this end. However, concerns about evaluation demonstration in novice academic writers’ literature reviews have been repeatedly voiced in academia. This study examines how Chinese English-major MA students utilize evaluative resources in different rhetorical stages in thesis literature reviews and whether in a way that facilitates building a coherent argument for their own studies. To achieve this, an integrated appraisal analysis applying Martin and White’s (2005) appraisal framework with a move analysis based on Kwan’s (2006) model of the move structure of thesis literature reviews is undertaken. Results show that the Chinese students generally manipulate evaluative resources in a way that is beneficial for realizing the purposes of different rhetorical stages in thesis literature reviews. However, they also have problems in deploying generic structure and constructing evaluative stances, which hamper weaving a strong argument in the texts. Findings of this study provide implications for teaching English academic writing in China and in other L2 contexts as well.

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.

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.028
GPT teacher head0.338
Teacher spread0.310 · 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