Syntactic complexity and connector use in the summary writing of L1 and L2 Canadian students
Bibliographic record
Abstract
Abstract The study compared the syntactic complexity and the use of logical connectors in summaries written in French by two groups of students: speakers of French as first language (L1) and peers in a French-immersion program (L2) from grade nine to university. Both L1 and L2 groups of university students demonstrated more general complexity in their written summaries than less mature writers. However, the use of phrasal elaboration was apparent just at the upper secondary and university levels with L1 students. Both groups generally tend to overuse causal connectors while underusing additive and adversative connectors compared to the author’s use in the source text. Yet both groups employ significantly more adversative connectors just at the post-secondary level. The only difference observed between L1 and L2 writers was in the diversity of connector words used in the summary, with the former group using a richer, more diverse vocabulary. Several measures for succinctness differentiated L1 and L2 students in early secondary with the latter group condensing the source text less than L1 students. Correlation analysis suggested that many measures of syntactic complexity and connector use are inextricably linked.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".