Bibliographic record
Abstract
Modern sociolinguistics, like other linguistic disciplines, seeks to use modern technology in its research. As a good example here can serve the attempt to develop a sociolinguistic classifi cation of written languages of the world undertaken by the scientists from the Laval University (Quebec, Canada) in 1988–2002. The main idea of the classifi cation was to measure the vitality of a language by determining the intensity of its social functions in different areas of communication. The written languages of a number of countries, such as China, India, were studied. The sixth volume of this international work consisted of two books devoted to the languages of Russia, where the sociolinguistic parameters of all languages of Russia were studied, except for the languages of national minorities. According to this international study of Canadian scientists, it was possible, fi rstly, to clarify the number of written languages of the world, secondly, to create a sociolinguistic classifi cation of world’s languages. However, for objective reasons the work was not completed, and the achievement of the above-mentioned possibilities remains the scientifi c task of the future.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".