KFL Program Building in the Era of Expansion: Innovative Local Strategies for Emerging Challenges
Bibliographic record
Abstract
ABSTRACT This is a comparative study on the developmental trajectories of Korean as a foreign language (KFL) program building, based on the experiences of the past decade by four large state universities in North America. All four programs offer at least a three-year language sequence and are currently seeking expansion toward a full Korean studies program within its own academic context. This article explicates how program building efforts have addressed emerging challenges that are often institution-dependent and program-specific. The following strategies are highlighted: (a) starting a new program through community engagement, (b) reconfiguring the curriculum with changing demographics, (c) strengthening the program by stabilizing enrollment, and (d) fine-tuning the curriculum for program expansion. Thanks to innovative and proactive strategies, each program has passed the first phase of development in practical language training and already offers a solid curriculum with a major/minor degree in Korean language and/or Korean studies. In addition, the article examines the ways in which the pedagogical and curricula practices employed by these schools have helped to establish program identity and sustain program growth. Finally, the article projects that commonalities as well as the local differences in the four programs would be useful in designing an assessment framework on KFL program evaluation.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".