Error Analysis: Approaches to Written Texts of Turks Living in the Sydney
Bibliographic record
Abstract
The purpose of this study is to describe the errors made by Turks living in Sydney, Australia in Turkish written texts. The mistakes identified in the texts were handled with the error analysis approach and evaluated according to their linguistic, cognitive processing, communicative, spelling and punctuation characteristics. Content analysis technique, one of the qualitative research methods, was used in the research. The study group consisted of forty-one people, aged between 10-25 years, living in Sydney, Australia in 2017. Participants were asked to create a text of at least 250 words by selecting any of the seven elective subjects in the written expression form. The texts were then examined one by one and the errors were analyzed under four headings: linguistic, cognitive processing, communicative, spelling and punctuation. As a result of the analyzed data in written expression texts, 951 linguistic and cognitive processing, 343 communicative, 230 spelling and 178 punctuation errors were detected. By analyzing the written texts under these headings, it is thought that the mistakes will be identified more easily and be beneficial for the language teaching process and everyone involved in this process, that the mistakes can be avoided more easily by focusing on more efficient and goal-oriented works and that they will save time.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".