Semantic Categories of Reporting Verbs across Four Disciplines in Research Articles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it