Proper Names in Slovene: Implications for defaults in inflectional morphology
Bibliographic record
Abstract
Inflectional systems generally contain default patterns, such as pl.-s in English, which are used in the absence of a reason to use a nondefault pattern.Contentious points include whether there can be more than one default pattern, whether proper names must follow the default pattern, and whether default patterns cover all words in the language.In Slovene, nouns are inflected for 6 cases and 3 numbers, and may be of 3 genders.Masculine gender appears to be the general default, showing the most widespread generalization, while feminine inflections generalize only to nouns that end in -a, including proper names.Female proper names that do not end in -a receive no overt inflections, with differences between surnames and personal names.Proper names do not have to follow default patterns, and may be ineligible for any inflectional rule at all.While standard linguistic and connectionist approaches can deal with the facts, the Marcus-Clahsen-Pinker approach finds the data challenging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".