Research based on the development trend of world language
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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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".