A Study of Nominalization in the Abstracts of Linguistic Academic Papers
Bibliographic record
Abstract
Nominalization is widely used in the linguistic academic papers and it has the potential to transform a complex sentence into a concise one, while abstracts in the linguistic papers are the condensation of the main content, so it is of great significance to analyze the phenomenon of nominalzation in the abstracts of linguistic academic papers. Based on the theory of grammatical metaphor, this paper attempts to figure out whether the five types of nominalization proposed by Halliday exist and further analyze which kind of function they perform. 40 academic papers will be collected, the five types of nominalzation will be identified and their frequency will be quantified. Finally, the detailed analysis about their functions will be made. After the analysis of the data, the author found that verb nominalization is used most frequently, while proposition nominalization is relatively rare. And nominalization in the abstracts can serve several functions: adjective nominalization and verb nominalzation can reflect the objective facts without personal attitudes and stance; conjunction nominalization can make the abstracts more concise. The purpose of this paper is to illustrate different types of nominalization and their functions existing in the abstracts, and to promote authors’ awareness of nominalization and improve their abstract writing ability.
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 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 | 0.000 |
| 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 | 0.000 |
| 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 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".