Bibliographic record
Abstract
With its many and diverse languages, including some with very long documented histories, its cultural diversity, and its widespread multilingualism - both the stable and transient kind - the Himalayan region is a treasure trove of empirical data for linguistic research on language typology and universals, historical linguistics, language contact and areal linguistics. Himalayan Languages contains contributions on Himalayan linguistics written by some of the leading experts in the field. The volume is divided into three parts: First, a general overview is given of the linguistic study of Himalayan languages and language communities. The second part offers synchronic studies of individual languages of the region (Indo-Aryan languages Shina and Kalasha, and Tibeto-Burman languages Belhare, Magar, Kinnauri, Classical Tibetan and Thangmi). The papers in the third part of the volume address topics in historical and areal linguistics, with an emphasis on the Tibeto-Burman languages of the region, discussing grammaticalization processes (in Sunwar, Newar, Seke, Tshangla and Bantawa) and the subgrouping of Tibeto-Burman.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".