Bibliographic record
Abstract
Utilizing Kloss’s concept of Ausbausprache (language as a sociopolitical construct), this article adopts the view that many languages in the world owe their language status to non-linguistic factors such as their speakers’ ethnic, cultural, and political affiliations, as well as language policy. It is thus possible that individuals who can readily understand each other in everyday conversation (such as two individuals living on either side of the Macedonian/Bulgarian border) can be deemed to speak different languages, while those who cannot understand each other at all (such as speakers of Shanghainese and Mandarin) can be widely perceived as speakers of the same language. This article is an account of how the South Slavic language formerly known as Serbo-Croatian came to be conceived of as a single, unified language due to a number of non-linguistic factors, and how it ceased to be considered a language once these non-linguistic factors were no longer present. Thus, apart from being a case study of how one particular European language was born and how it died without any significant change in linguistic reality on the ground, the present article serves to reinforce the theoretical notion of Ausbausprache as a crucial concept for defining what a language is.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".