‘This novel is not totally full of tears...’: Graduation Resources as Appraisal Strategies in EFL Students’ Fiction Book Review Oral Presentation
Bibliographic record
Abstract
Today, scholars in the field of text analysis are increasingly interested in studying evaluative language. However, few, if any, studies on classroom language have examined EFL students' fiction book review oral presentations from the perspective of the appraisal model. This study aimed at examining students’ use of graduation resources as part of appraisal strategies in classroom discourse, as well as lexico-grammatical resources for coding these strategies in texts. Graduation, in functional perspective is the sub-system of appraisal that is connected with force and focus in creating interpersonal meaning. The dataset comprised three transcripts of students’ fiction book review oral presentations in scientific presentation course, comprising 7.593 words in total. The data were examined quantitatively to identify the statistical variations in utilizing graduation resources in EFL students’ oral presentations. The preferences for lexico-grammatical resources for the construal of these strategies were also illustrated through a qualitative analysis. The results of the study reveal that the classroom discourse of EFL students’ fiction book review oral presentation is loaded with graduation resources. The results of the study show that all students used all graduation resources, specifically force and focus. In terms of force, the sub-systems of intensification (e.g., just, so, never, quiet, full of) and quantification (e.g., only, one of, some of, closely, etc) were applied. Meanwhile, in terms of focus, students only used the sharpen sub-system (e.g., originally, especially). The sub-systems within the system of graduation were shown to serve as strong tools developing the student’s skill to have critical thingking competence.
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 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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".