Functions of names in Romanian and foreign fairy tales – between cultural diversity and identity
Bibliographic record
Abstract
: This paper is aimed at analysing the functions of names in Romanian and foreign fairy tales by highlighting similarities and differences of names in various cultures (English, French, German and Romanian). In comparison with names in real life, proper names in literature feature more functions than that of identification, as such names are varied from a semantic and structural perspective. The relationship between a name and its bearer is motivated; thus, literary names are generally more innovative and their structure is much more complicated than in the case of names in real life. One finds it impossible to talk about a set of stable functions of names in fairy-tale discourse, since it is impossible for one to separate their hierarchy from the text in which they appear. The functions of proper names in fairy tales are inferred by means of analogy based on the more general functions of verbal communication defined by Jakobson (1964: 88–94). In the framework of fairy-tale discourse, the functions are not independent, but actually depend on one another, as names in fairy tales are polyfunctional in themselves. Several functions occur: dominant (referential), as well as subjacent or latent (allusive, camouflage, localisation, didactic-educational, expressive, semantic and sociological) functions. The research framework of this paper is onomastics, but the approach is transdisciplinary. The methods belong to pragmatics, semantics and semiotics. The corpus was compiled from anthologies of folktales and modern fairy tales.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".