Bibliographic record
Abstract
Journal of the International network for Korean Language and Culture 16-2, 31-63. This study focuses on the concept of communicative competence necessary for learners who want to learn language, such as a foreign or second language, and how the paradigm has changed. In Section 2, the perception of communicative competence comes from the concepts distinguished by Chomsky (1965): “linguistic competence” and “linguistic performance.” Section 3 examines ’ view of communicative competence as an educational paradigm, explicitly using the term “communicative competence.” Hymes clarifies communicative skills, such as “knowledge about the speaker's speech and evaluation of the uttered contents,” in a practical and conversational environment, dividing it into four categories: 1) “Possibility,” 2) “Feasibility,” 3) “Appropriateness,” and 4) “Occurrence .” In Section 4, Canale and Swain (1980, 1983) examine the subdivisions of these abilities, with a particular focus on the subordinate abilities related to operations and strategy, as well as psychology and use, rather than previous language knowledge. In Section 5, this paper looks at the CFB (Canadian Language Benchmarks) and CEFR (Common European Framework of Reference for Languages), the method that led to the paradigm of evaluation through the concept of communicative competence.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".