Research timeline: Second language communication strategies
Bibliographic record
Abstract
Speakers of a second language (L2), regardless of proficiency level, communicate for specific purposes. For example, an L2 speaker of English may wish to build rapport with a co-worker by chatting about the weather. The speaker will draw on various resources to accomplish her communicative purposes. For instance, the speaker may say ‘falling ice’ if she has forgotten the word ‘hail’ or may repeat the last few words of her interlocutor's utterance to show that she is listening and engaged. The termcommunication strategies(CSs) refers to the strategic use of various resources (both linguistic and non-linguistic) for communicative purposes. While speakers also use CSs in their native languages (L1s), research on L2 CS use is particularly interesting because speakers’ L2 linguistic resources and the associated cognitive processes are typically less developed, compared to those in their L1. Therefore, for L2 users to accomplish their communicative purposes in the L2, it is important that they effectively use the resources available to them. This research timeline presents key developments in theoretical understanding and empirical research targeting L2 CSs, mainly in oral communication. The timeline places particular emphasis on the evolution of theoretical approaches to the study of CSs and the consequent expansion of research in terms of the nature of participants, speech samples, and analytical tools used.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.088 | 0.029 |
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".