Bibliographic record
Abstract
Today’s technology allows quick and easy communication with speakers from a variety of language backgrounds, and the communication of online participants is predominantly in English. Although much is already known about the attitudes of Croatian students towards their own English pronunciation (e.g., Lütze-Miculinić, 2019; Josipović Smojver & Stanojević, 2013, 2016; Stanojević & Josipović Smojver, 2011) or about different English varieties (Drljača Margić & Širola, 2014), there has been no research regarding students’ identification of different English varieties in Croatian context. Previous studies (Williams, Garrett, & Coupland, 1999) have shown that listeners can categorize unfamiliar speakers by dialect with about 30% accuracy. Apart from familiarity, an important role in variety recognition is played by regional closeness and exposure to the variety. The present research is set out to study how accurately students can identify individual speakers of different regional and EFL varieties of English. The study was conducted on 68 first-year English students who completed an anonymous questionnaire. The items for the questionnaire were based on Alić (2021) and Paunović (2009). The study was based on a verbal-guise technique, where participants listened to 10 speakers reading out the same paragraph. Croatian students showed poor results in variety recognition. They had the best identification results when listening to Croatian speaker speaking English (76.9% of correct identifications), which is of no surprise as he was the only EFL speaker and the students were familiar with this type of accent. They had the most problems identifying the speakers from South Africa (13.4%) and Northern Ireland (10.8%). The results show that students probably till operate with very broad concepts, like "British" or "American" English, since they were unable to pinpoint the speakers from South England (32.8%) or California (19.7%), varieties that can often be heard in various settings. The Californian speaker was identified as a speaker from New York (19.7%), Southern USA (18.1%) and Canada (16.6%), which shows that subtle differences in regional identity are lost to the untrained ear, and that familiarity sometimes does not play an important role in variety identification.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".