Analysis of Metadiscourse Features in Argumentative Writing by Pakistani Undergraduate Students
Bibliographic record
Abstract
Metadiscourse involves the interaction between the reader and the writer of the text in the overall process of communication. Metadiscourse not only guides the reader to understand the primary message of the text through structure and content, but also it intimates the reader with the particular slants and perspectives in the primary discourse. The students have to master the use of Metadiscourse in their writings. The purpose of this study is to examine the distribution and frequency of Metadiscourse features used by Pakistani undergraduate students in their argumentative essays and to analyze roles played by these particular features. Moreover, the research explores the extent of appropriateness and inappropriateness in this particular text as well. Hyland’s Interpersonal model of Metadiscourse (2005) was adapted to conduct the present study. AntConc 3.4.4 software is used for corpus analysis of the text. Findings show that Pakistani undergraduate students are more comfortable with using Interactional Metadiscourse 61% rather than Interactive dimension 39%. It has been observed that undergraduate students used high score of self-mentions 37% and engagement markers 37%. Endophoric markers were not used by these students 0%. Findings have considerable importance, as they assist the learners to figure out the problems of the students regarding the use of Metadiscourse. Trainings should be given to the students to use these features appropriately.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".