Bibliographic record
Abstract
In 1808, the German Romanticist Johann Fichte contended that "men are formed by language far more than language is formed by men." Following this and understanding that language is closely tied to personal identity, to what extent can language become a means to influencing identity and, through that, influencing actions? This presentation will look at the role that language played throughout twentieth century China by focusing on how language was employed as a nation-building instrument in China’s transformation from empire to nation-state by promoting a common identity where previously none had existed. This study looks at the case of China for it presents a continuous group of people affected by three different governments in less than a century, who experienced three different nationbuilding campaigns, where three different sets of language policies were applied. By looking at thelanguage policies and planning during the Republic of China from 1912-49, the Maoist People’s Republic of China during the 1950s, and the shift in policy in the 1970s and 1980s with Deng Xiaoping, I examine how and why language policy changed and how successful the policies were in affecting an idea of nation and national identity amongst China’s populations. By studying the motivations, aims, and consequencesof language planning this study leads to an understanding of why nations engage in language planning and acknowledges the power, or lack thereof, that deliberate language reform and policy can have ininitiating greater social and political change.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".