Towards Understanding Tajikistan’s Sociolinguistically Complex Language Ecology: Historical Development, Current Status, Issues, Research, Policy and Practice
Bibliographic record
Abstract
Tajikistan, at the heart of Central Eurasia, had a population of 7,563,687 in 2010, estimated recently to be almost 10,084,935. Named for its majority nationality, Tajikistan has many other nationalities, most with their own language. This article explores what is known about the historical, development and current status of multiple languages in Tajikistan’s linguistic tapestry. We provide a tentative overview of Tajikistan’s evolving language ecology from earliest times when a range of Eastern Iranian languages were dominant, to the reduced use of Eastern Iranian languages following the entry of Arabic and New Persian (a western Iranian language) into the ecology with the Arab conquest, and the subsequent entry of Turkic languages, and more recently the entry of Russian under the late Russian empire and its spread under the Soviet Union. Following independence in 1991, a shift in balance among domains of use of Tajik and Russian has been ongoing at the same time as international languages, especially English, have entered Tajikistan’s language ecology. We review the current state of knowledge about contemporary sociolinguistic dynamics, monolingualism and plurilingualism in society, where the titular language, Tajik/Persian, is in interaction with local, regional and global languages. Against the background of changing post-independence language and language-in-education policies, we discuss the prospects for monolingual, multilingual and plurilingual education in Tajikistan among efforts to promote the official language, Tajik, and to provide minority language education, while also developing proficiency in foreign languages in Tajikistan, through initiatives such as English-medium instruction.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".