The Case for Subject-Verb Dependency Distance as a Measure of Complexity and Readability
Bibliographic record
Abstract
Complexity is a core concept in academic language, central to the development of both writing proficiency and readability. The measurement of complexity and readability is evolving, with large-grained measures like sentence length being supplemented with more fine-grained measures. Complexity in academic texts has increased, particularly with the use of noun phrases with complex postmodification. However, postmodification of noun phrases is a locus of complexity that has been overlooked. In noun phrases where the head is the subject of a verb, postmodification will necessarily intervene between that subject and verb. Longer distances between syntactically dependent words increase complexity and reading difficulty. This paper argues for incorporating subject-verb dependency distance in measures of complexity and readability in academic writing. The study analysed subject-verb dependency distance in a 110,633-word corpus of published scientific writing from generalist and specialist journals, comparing journal types and highlighting how noun phrase postmodification influences this measure of complexity. This paper also discusses pedagogical implications and presents sentence transformations to illustrate how writing instructors can raise awareness of subject-verb dependency distance in the context of phrasal complexity.
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 | 0.016 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".