Can Separatist Ethnonationalist States Create Inclusive Multilingual Education Policies? Evidence from The Iranian Plateau
Bibliographic record
Abstract
In this article, the author draws on evidence from newly emerged states’ language policy and planning in the Iranian Plateau, and its surrounding Silk Roads region, in order to illustrate that the European nation-state model has been a major cause of linguistic discrimination in this region although separatist movements can assume that establishing a new state can protect their ethnic languages. The adaptation of this form of governance in these territories has seriously damaged the region’s organic linguistic repertoire. The failure of the modern state to provide an inclusive language policy has long been observed and discussed in the field of sociology of language. In this article, the author provides examples to show that newly emerged nation-states oppress the Indigenous minority languages within them and fall short of satisfactorily addressing the language issues of immigrants because of their narrow and inflexible definitions of nationhood and national identity. Additionally, the author illustrates that nation-states not only target minority languages, but also they undermine the very ethnic language that they claim to promote. This happens by elevating the status of one variation of the ethnic language and at the same time devaluating the other dialects and accents. The author concludes that investment in nation-statism may or may not lead to the creation of a state that is respectful of linguistic human rights. A more meaningful investment in terms of language planning is organizing anti-discrimination movements both in current larger states or possible future ethnic states.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".