Bibliographic record
Abstract
Serbo-Croatian Abstract: Uzimajuci za povod poglavlje iz zbornika Jezik u Bosni i Hercegovini koje je napisao urednik Svein Monnesland, pokazuje se neispravnost niza tvrdnji u tom poglavlju, npr. tvrdnje da postojanje nacije automatski znaci i postojanje zasebnog jezika, ili tvrdnje da kod jezika i njegovog imenovanja nije bitna kolicina razlika ni međusobna razumljivost ni lingvisticka srodnost. Utvrđuje se neupucenost autora u sociolingvisticku teoriju o policentricnim jezicima i u nepodudaranje jezika s kulturom i religijom. Također se ukazuje na autorovo nedovoljno poznavanje jezicne situacije u Njemackoj, Svicarskoj, Kanadi i u raznim drugim državama. Usput se ispravljaju pogresni navodi o jezicnom unitarizmu u bivsoj Jugoslaviji do 1990. godine, a i krive tvrdnje o danasnjoj jezicnoj praksi u Hrvatskoj. English Abstract: The article critically analyzes the chapter from the book Language in Bosnia and Herzegovina that is written by editor Svein Monnesland and shows the incorrectness of claims in that chapter, such as the existence of a nation automatically means the existence of a separate language, the amount of language differences and mutual understanding do not matter in sociolinguistics. The author's ignorance of sociolinguistic theory about polycentric languages, of the mismatch between language, culture and religion, and of the linguistic situation in Germany, Switzerland, Canada and various other countries has been criticized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".