Stem Restructuring in the Development of Common Slavic *<i>nogbtb</i>and the Like in Balkan Slavic Dialects
Bibliographic record
Abstract
The Bulgarian literary language, together with the large majority of Bulgarian dialects and a sizeable number of Macedonian dialects, exhibits a disyllabic stem in both the indf. and df. sg. forms of masc. nouns from three distinct etymological classes: 1) original CS1 disyllabic stems (e.g., *nogbtb, *коnьсь > Bg нокъm, нокътът, Mac конец, конеyоm); 2) epenthetically disyllabic stems with sonorant auslaut (e.g., *og[b]n’, *v ĕ t[b]r > Bg огън, огънят); 3) epenthetically disyllabic stems with non-sonorant auslaut (*vos[b]k, *moz[b]k/g- > Bg восък, восъкът, Mac мозок, мозокот). The same set of reflexes is found in Literary Macedonian, with the exception of two stems from the second class, viz. огнот and eempom ~ Bemepom, and the neuter variant лакто of шкот, which probably is derived from an earlier masc. df. *лакто[т]. In contradistinction to these developments, many dialects of the southern and northeastern peripheries exhibit a range of alternative conservative and innovative monosyllabic stem reflexes (e.g., нокт, концот, орлът, воск, музго). The present study exlores in detail the morphophonemic origins of these exceptional reflexes and examines the closely related analogical extension of the disyllabic stem in certain dialects to the Balkan Slavic plural of the word *nogbtb (cf. *nokti/-e >*nokbti /-e).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".