Bibliographic record
Abstract
Abstract The paper explores deonymic nomination, i.e. the formation of appellatives (eponyms) from proper names. By an eponym, any type of non-onymic unit formed from proper name is understood. The analysis is conducted using a database of 1,250 eponyms from Slovník slovenských eponym (Dictionary of Slovak Eponyms; Ološtiak et al., 2018) and a theory of lexical motivation as a methodological background is applied. From this viewpoint, formation of eponyms can be characterized as the loss of onymic motivation (onymic demotivation) and at the same time the acquisition of another type of motivation depending on the type of word-formation process (in a broader sense). In this regard, a word-formation process is understood as any way of coining a new lexeme (one-word unit, multiword expression, new meaning, abbreviation, borrowing etc.). Eponyms are frequently coined by derivation (word-formation motivation, e.g. Albert ‘Albert County (Canada)’ → albertit ‘albertite’, Heine → heineovský ‘of or relating to H. Heine’) and by semantic shift with no part-of-speech change (semantic motivation, e.g. Pascal → pascal ). Other processes are rare: part-of-speech change with no shift in morphemics (morphological motivation, e.g. Ježiš (noun) ‘Jesus’ → ježiš (interjection) ‘Jesus, an expression of emotion – surprise, anger, shock etc.’), abbreviation (abbreviation motivation, e.g. Mikojan + Gurevič → mig ‘a military aircraft’). In Slovak, most of the eponyms are loanwords (97.4%), thus, a special position is occupied by interlingual motivation.
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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 | 0.001 | 0.069 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".