Investigation of Latvian Language Program Management Models in Major Worldwide Universities
Bibliographic record
Abstract
Compared with some European countries and America, Latvian language teaching is relatively new practice in China.Since the launch of the first Latvian language program at Beijing Foreign Studies University (BFSU) in 2010, three management models have been implemented and evaluated.For almost 10 years, approximately 130 students have been involved in Latvian language learning, either in elective courses or in bachelor degree programs.Positive results have been observed, but problems still exist.Lack of experience, limited staff and materials lead to unsystematic teaching practice and make it difficult to fulfil the aims.To look for solutions, one effective way is to learn from others' experience.This investigation took "Latvian language program management model in tertiary level" as subject, used the methods of document analysis, semi-structured interview and observation to conclude and compare the program management models in the main universities which teach Latvian language.During the period of March, 2018 to January, 2019, 4 teachers and 3 students from 5 universities were interviewed.Besides, although Latvian language teaching in Japan is not organized in tertiary level, considering the similarity of learning style, the teaching practice in Japan was also included in the investigation.Data analysis compared the key factors in the 6 cases and presented mainly three management models, which offered good examples for teaching practice in China.The case in America especially showed an effective solution to the problem in the Discipline-directional module at BFSU.Furthermore, it was also observed that the program management in different universities is quite enclosed, and the programs seldom have multilateral cooperation.It also recommended to promote cooperation among the programs in different countries, in order to maximize the effectiveness of teaching resources.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".