Expressions of Modality Associate Degree Business Explanation Essay Conclusions: A Functional Linguistic Perspective
Bibliographic record
Abstract
The rhetorically complex concluding components of academic written texts often challenge novice writers, having to summarise their arguments and stance, and offer prospective comments on future developments concerning the subject matter. With an aim to elucidate the lexicogrammatical expressions of such prospective comments in essay conclusions, the present study adopts the system of modality informed by Systemic Functional Linguistics (SFL) to examine the conclusions of explanatory essays written by non-native English speaking associate degree business students. The analysis compares the modality expressions deployed in high- and low-graded essay conclusions, including modality types, explicitness, subjectivity and value. The analysis also investigates how the modality resources are combined for providing more than one comment in the conclusion. The findings show that high-graded texts have a more balanced choice of modality, less overly assertive features and more prospective comments, while they still require improvements on a more consistent deployment of modality features. This paper concludes with a brief discussion on teaching implications of the present study, in that writing instruction can make explicit the functions of different modality expressions and equip students with the linguistic repertoires appropriate for more formal and technical academic written registers.  
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".