Acquisition of English Discourse Markers by Chinese L1 Speakers Learning English in the US: Frequency and Social impact
Bibliographic record
Abstract
Sankoff et al. (1997) indicated in their research on discourse markers (DMs) used by anglophones in Montreal that the mastery of DMs is a good indicator of the non-native speakers’ integration into the linguistic community. As DMs, especially the informal ones, are not taught explicitly in language classes, their acquisition could only be fulfilled by extracurricular contact with native speakers. Despite fruitful works done on DMs in both native and non-native speech, to our knowledge, most of them deal with only one or several DMs at a time without providing a comparable complete list of frequency of DMs in non-native speech. In this article, by exploring the data from 29 semi-guided sociolinguistic interviews conducted in English with nonnative speakers in the US, we established a list of frequency of 72 DMs employed by Chinese L1 speakers learning English. By conducting statistical tests, we examined the impact of some extralinguistic factors relevant to non-native speakers in their use of DMs. Our results showed that gender and social network are the two most influential factors for informal DMs, while the age factor seems to be the weakest for all DMs.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".