"Langscapes" and language borders: Linguistic boundary-making in northern South Asia
Bibliographic record
Abstract
Drawing on examples from the linguistically-diverse Himalayan region, in this contribution we explore three main questions. First, we ask how language boundaries both contribute to and defy the imagination of the nation-state. Second, we investigate how such boundaries are transcended and become redefined through increased mobility and technological innovation. And third, we examine what it means for languages to become detached from the landscapes in which they were traditionally situated and historically spoken. Unfixed and unfixable, languages resist the limitations and constraints of nation-states—both colonial and contemporary—that strive to delineate their boundaries along "clear" and often monolingual lines. In the Himalayan region in particular, plural linguistic identities challenge reductive national logics that seek to bind or appropriate languages for hegemonic and ideological goals. Not only are national borders decreasingly relevant for the maintenance and transmission of languages, but the global dispersal of people and the languages they speak, sign and write are combining with accessible digital media to transform internally maintained language borders as well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".