Research based on the development trend of world language
Why this work is in the frame
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Bibliographic record
Abstract
Multicoloured languages play an irreplaceable role in the whole world as a useful communication tool. With the development of technology and science, varieties of languages have an ideal prospective tendency to evolution during the long and wonderful history. Will they be thriving or decaying?To begin with, aimed to gain general tendency about the quantity of languages’ speakers, we employ the Grey prediction to capture associative curve which can be seen in figure(1). From the trend of this vivid figure, we not only can come to the conclusion that the number of English and Chinese users tend to increase but also find that Spanish development will reach the period of stagnation.Secondly, for further improvement, we take birth rate, death rate, economic factors and the immigration into consideration and establish the language communication model. This model is deduced from the population prediction model and virus transmission model. After data normalization, the eventual curve indicates that current top-ten languages seem to be replace by other languages. This transformation phenomenon also occurs among such top-ten languages. For instance, Hindustani will replace Spanish in the future when seen from table(1).What’s more, after predicting the migration pattern, we can draw the conclusion that some range of languages’ dissemination has obvious change. As show in vivid figure(14), we know English will popularize widely among neighbouring countries such as Canada, Mexico, Cuba and Russia.Moreover, with regard to how to manage international offices’ quantity and locations in the world, we construct the efficiency model with combination of the Bayes’ probability theory and Fussy comprehensive assessment. As a result, we obtain 9 optimal plans to establish the international offices. Intelligible result is showed in table(4) and table(5).Furthermore, taking the variation of global communication and shortage of nature resource into consideration, therefore, we propose the international company to set up no more than 5 offices. And 5 offices tend to be the most optimal plan.In short, our model is reasonable and feasible, which can accommodate to different situation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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 it