Salience in EFL speakers’ perceptions of formality: (In)formal greetings and address forms combined with (in)formal nouns, verbs, and adjectives
Bibliographic record
Abstract
This study explores whether English-dominant (ED) speakers and speakers of English as a foreign language (EFL) perceive the same degrees of formality in combinations of (in)formal greetings (Hi/Dear) and address forms (informal First Name/Ms. Last Name) with (in)formal nouns, verbs, and adjectives (Latinate/Germanic). It also explores which of these variants the two groups perceive as salient in communicating formality. Twenty-five ED undergraduates in Canada and 27 EFL undergraduates in Slovakia rated the formality of 20 sentence-length examples of business email correspondence and identified features that were the primary basis for their formality rating. Distributions of 11 of the formality ratings were statistically significantly different in the two groups (with most effect sizes ranging from small to medium), and trends in the reports of salient features suggested that the EFL speakers focused on the formality of address forms more frequently than did the ED speakers. The findings are discussed in relation to infelicitous interlingual transfer and strategies for developing pragmatic 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".