Bibliographic record
Abstract
Abstract Retroflexion in South Asia has been the subject of at least two previous typological studies: Ramanujan and Masica (1969. Toward a phonological typology of the Indian linguistic area. In T. A. Sebeok (ed.),Current trends in linguistics, volume 5: Linguistics in South Asia, 543–577. Paris: Mouton) and Tikkanen (1999. Archaeological-linguistic correlations in the formation of retroflex typologies and correlating areal features in South Asia. In Roger Blench & Matthew Spriggs (eds.),Archaeology and language IV: Language change and cultural transformation, 138–148. London & New York: Routledge). Despite their many virtues, these studies are limited by the size of their data samples, their dependence on qualitative data without quantitative analysis, and their use of hand-drawn maps. This paper presents the results of an entirely new survey of retroflexion in South Asia – one that incorporates a larger language sample, quantitative analysis, and computer-generated maps. The study focuses on the genetic and geographic distribution of various retroflex subsystems, including retroflex obstruents, nasals, liquids, approximants and vowels. While it is possible to establish broad statistical correlations between specific types of contrast and individual language families (or sub-families), the study finds that the distribution of most retroflex systems is more geographic in nature than genetic. Thus, while retroflexion is characteristic of South Asia as a whole, each type of retroflex system tends to cut across genetic lines, marking out its own space within the broader linguistic area.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".