The effect of mode variation on syntactic complexity in adult E.S.L. composition writing
Bibliographic record
Abstract
The purpose of this study was to determine whether variation in mode of discourse would produce significant differences in syntactic complexity, as measured by mean number of words per T-unit, in compositions written by-adult- students of English as a second language. To answer this question, compositions were collected from eight classes of Advanced level students in the English Language Training Department at King Edward Campus in Vancouver. Each student in the study wrote eight compositions over an eight week period in the Fall of 1981. Two different topics were assigned in each of four modes with the topics assigned to class in random order. The compositions of those students who wrote on every topic at the appointed time (N=61) were divided into T-units, words were counted and words per T-unit calculated. The mean number of words per T-unit per mode was then determined for description, narration, argument, and exposition. Differences, in mean number of words per T-unit for six pairs of modes were tested for significance at the .05 level. The six pairs, narration-description, narration-exposition, narration-argument, description-exposition, description-argument, and exposition-argument were analyzed using a t-test for dependent measures. The results indicated that there were significant differences in W/TU between five of six pairs with no significant difference only for narration-description. The order of complexity indicated from these results was N=D< A< E. The order of complexity found in this study is similar to that found in other first and second language studies, in that argument and exposition were shown to produce greater syntactic complexity than either narration or description. Other results found in this study showed that a high proportion of students wrote "out of mode" when given tasks in argument and exposition whereas almost all subjects remained "in mode" when writing in description or narration. The results of this study showing syntactic complexity to be a function of mode of discourse suggests strongly that where complexity is a factor of consideration either in research or evaluation, mode must be controlled or results interpreted with the recognition of a potential mode effect.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".