Uncovering the Common Linguistic Errors in Student Journalists’ Unedited News Articles: A Comprehensive Analysis
Bibliographic record
Abstract
English language learners encounter difficulties in mastering grammar and vocabulary, which can significantly influence their writing skills. This study analyzes the most common writing errors and their linguistic classification in student journalists’ unedited articles; it also utilizes a qualitative-descriptive design and Corder’s Error Analysis Model for data analysis. Ten selected unedited news articles written by student journalists were examined. Findings revealed that the student writers have common linguistic errors: mechanical, morpho-syntactic, and lexico-semantic. The findings imply that student journalists are not exempted from committing linguistic errors that may affect information decoding despite being trained and exposed to writing activities. The findings have broader implications as student journalists may serve as representatives of English language learners in their institution or community. Schools may develop remedial programs to provide ample practice opportunities using interactive, technology-based tools for grammar and vocabulary. English teachers may create instructional materials and develop community extension and outreach programs to address grammatical learning gaps.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".