Analyzing online postings as social behavior for L2 instruction of (im)politeness: Sensitizing advanced learners of German or English
Bibliographic record
Abstract
Abstract This study takes a discursive approach (cf. Watts 1989, 2008; Locher 2004, 2006, 2011, 2012; Locher and Watts 2005, 2008; Mills 2011; van der Bom and Mills 2015), to analyze online postings as social behavior for use in other/second language (im)politeness instruction. The criteria for inclusion are authenticity, complexity, diversity and feasibility. This means the postings need to (1) be created for a social purpose, not an instructional one; (2) be pragmatically rich and exemplify evaluations of (in)appropriate behavior; (3) illustrate variety in their underlying social norms and values; as well as (4) lend themselves to examination by learners and provoke reflection in conjunction with participatory learning activities. The analysis found that postings from online fora can meet these criteria and identifies (a manageable number of) posts for which participatory learning activities were developed. In combination the posts and the activities are used to enhance awareness of the complexity of (im)politeness in social encounters, especially in intercultural interactions, and the omnipresent and emergent nature of (im)politeness. They have been used in a university setting to sensitize the L2 learner to differences in the views, judgments and negotiations of what constitutes polite or impolite behavior between as well as within speech communities.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".