Bibliographic record
Abstract
Responding to calls for research into measurable English language outcomes from individual language support consultations at universities, this study investigated the effect of individual consultations (ICs) on the academic writing skills and lexico-grammatical competence of students who speak English as an additional language (EAL). Attendance by 31 EAL students at ICs was recorded, and samples of their academic writing texts before and after a 9-month interval were compared. Participants’ academic writing skills were rated, and lexico-grammatical irregularities were quantified. No statistically significant positive shifts manifested, due to the relatively short research period and limited participant uptake, but there were encouraging predictors of future shifts given continued utilization of the service. First, although a Wilcoxon signed-rank test showed no association between attendance at ICs and shifts in academic writing ability, a Spearman’s rho calculation suggested a tentative relationship to positive pre–post shifts in three academic writing sub-skills: Task Fulfillment, Grammar, and Vocabulary. Second, instances of four common lexico-grammatical irregularities (subject/verb, wrong word, plural/singular, and punctuation) declined at post-testing. Although only regular, sustained attendance would produce statistically significant shifts, there is a potential association between participants’ use of ICs and improved academic writing skills/lexico-grammatical competence.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.009 |
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".