Minorities’ Heritage Language Planning and National Multilingual Capacity Building
Bibliographic record
Abstract
As an important part of a nation’s soft power, national multilingual capacity refers to a nation’s ability to use a variety of languages acquired in dealing with domestic and international affairs in the development of a nation. The nation-security-oriented language planning in the post-9/11 America is closely related with the teaching, using and developing of the minorities’ heritage languages, which has to some extent facilitated the America’s national multilingual capacity. Taking National Security Language Initiative proposed by the American federal government as an example, this paper suggests that minorities’ heritage language planning be an endogenous shortcut to build the national multilingual capacity. Furthermore, the relationship between minorities’ heritage language planning and national multilingual capacity building is established by matching the five key parameters in heritage language planning with the five components of national multilingual capacity respectively, i.e., exploring the correlations between languages planning, talent planning, education planning, industry planning, policy planning and national multilingual resources capacity, individual’s multilingual capacity, national multilingual education capacity, national multilingual service capacity and national multilingual management capacity in detail by using an analytical method.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".