Semantic Categories of Reporting Verbs across Four Disciplines in Research Articles
Bibliographic record
Abstract
This paper investigates semantic categories of reporting verbs across four disciplines: Accounting, Applied Linguistics, Engineering and Medicine in research article genre. A general corpus of one million words and sub-corpus (for each discipline) were compiled from a total of 120 articles representing 30 articles from each discipline. In this study, two levels of analysis were conducted. Firstly, I randomly selected five articles from each discipline and read and reread each article identifying what reporting verbs are used, in what context are used and why such reporting verbs are used. This process enabled me to identify semantic categories of reporting verbs. Secondly, on the basis of the identified list of semantic categories of reporting verbs, I used the list in generating concordance output for quantitative textual analysis of each sub-corpus of the four disciplines, as well as the general corpus. The results of the study show that writers from both Accounting and Applied Linguistics are having a high frequency of reporting verbs than writers from Engineering and Medicine disciplines. It also shows that there are certain commonalities and differences between the disciplines. For example, all the disciplines are having frequency of the three semantic categories of reporting verbs but with certain degree of variations. The study recommends raising awareness of students on semantic categories of reporting verbs. The results could also help EAP/ESP teachers in designing course materials for discipline specific reporting verbs. It could also be helpful for textbook course designers in developing textbooks for teaching reporting verbs.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| 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".