How (not) to be rude: Facilitatingthe acquisition of L2 (im)politeness
Bibliographic record
Abstract
Abstract This article argues for frequent targeted teaching of relational language use or (im)politeness in the L2 classroom. The approach presented here draws on authentic data in the target language and in the language of instruction, which are readily available online. It encourages the learner to make use of their multilingual resources and is exploratory in nature, allowing for a deep engagement with (im)politeness, viz., an extensive array of semiotic features invested in the co-construction of social relations in every social interaction. Working at the interface of (im)politeness studies, intercultural pragmatics, interlanguage pragmatics, and language pedagogy, and undertaken from the perspective of interpersonal pragmatics and relational work, the qualitative analysis focuses on the collaborative work products from participatory learning activities of intermediate to advanced learners of German at a large North-American university. Results show the learners’ raised awareness and broadened knowledge. In particular, learners became aware that what is judged as (im)polite is dependent on the relationship of the interactants, the gender of the interactants, the sociocultural background, norms, values, and believes of the interactants, the context of the interaction, the affiliations of the evaluator, the sociocultural background, norms, values, and believes of the evaluator, etc. Results also suggest that some of the learners need to develop their pragmalinguistic skills further to fully participate in the evaluation of pragmatically rich target language discourse. Additional studies are needed to explore the impact on the learners’ interactional 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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".