Uralic Numerals: Is the Evolution of Numeral System Reconstructable? (Reading new Václav Blažek's book on numerals in Eurasia)
Bibliographic record
Abstract
A new book on the history of numerals in the languages of different language families of Eurasia appeared two years ago (Bla zek 1999), a chapter of which is devoted to the Uralic numerals. Its author, Dr. Vclav Bla zek, is known not only as a researcher of Afro-Asiatic languages which is his main field, but also -and may be even more -as a scholar belonging to the Nostratists' family. The last direction determines his interest in the Uralic languages, too. His inclination to Uralistics, which might be marginal to himself and certainly not so important, is still sometimes very promising: e.g., at least the best of new Aryan etymologies for Finno-Ugrian words suggested during the past two decades belong to him (F.-Mord. *ak ster 'unfertile' < Aryan *a-k saitra-'uncultivated'; Vog. (Pelym) se sw" e etc. 'hare' < Aryan * sasa-'id.'; F.-Perm. sik st 'wax' < Aryan * sik sta-'id.' (Bla zek 1990 : 40-43)).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".