Speech Errors in English as Foreign Language: A Case Study of Engineering Students in Croatia
Bibliographic record
Abstract
The study reported in this paper investigates the frequency and distribution of speech errors, as well as the influence of the task type on their rate. The participants of the study were 101 engineering students in Croatia. A recorded speech sample in the English language (L2) lasting for approximately ten hours was transcribed, whereby more than three and a half thousand speech errors were recorded. Morphological errors were dominant due to a significantly frequent omission of articles. The distribution of different subcategories of lexical errors pointed to a relatively low frequency of unintended L1 switches, indicating that the participants were able to separate the two languages during lexical access. Statistical testings of the influence of the task type on speech errors displayed that the retelling of a chronological order of events resulted in a significantly higher rate of syntactic errors if compared to other tasks. Due to limited attentional resources and insufficient knowledge, the speaker cannot process the message within the time constraints. The rate of lexical and phonological errors depended on the frequency of use, that is, less frequently used words were more susceptible to lexical errors than high-frequency words. The retelling of a chronological order of events is a demanding task, for this reason, this task type should be more practiced in foreign language teaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".