Evaluation in Moves: An Integrated Analysis of Chinese MA Thesis Literature Reviews
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".