КВАЗІТОПОНІМИ ТА КОНОТАТИВНІ ТОПОНІМИ НА ПОЗНАЧЕННЯ ПРОВІНЦІЙНОСТИ В НЕФОРМАЛЬНОМУ МОВЛЕННІ ФРАНКОФОНІВ
Bibliographic record
Abstract
The aim of the article is to investigate the expression of the category of provinciality in quasi-toponyms and connotative toponyms used in the informal speech of Francophones. The actuality of the research is due to the general interest in the problem not only at the scientific but also at the amateur level. A number of phonetic, morphemic, structural and semantic features of these names are revealed. It has been found that the meaning of provinciality is expressed mainly by quasi-toponyms, which are either fictitious names or hybrid names, which contain real toponyms or their elements, but do not have a real referent. Clearly defined connotative toponyms denoting provinciality are less numerous, but this group of proper names has the potential to expand. Connotative place names in some cases denote quite large cities, but are located far abroad. The connection between the phonetic structure of quasi-toponyms and their semantics is revealed. Onomatopoeia, which refers to the sounds of the countryside (especially Quebec), phonetically embedded, albeit hidden obscenities (mainly France), are characteristic features of quasi-toponyms. From the point of view of morphemes, quasi-toponyms are characterized by the use of diminutive suffixes and pseudo-suffixes, which are also observed in real toponyms, based on which quasi-toponyms are formed. One-component quasi-toponyms are the least numerous and the least dynamic among all structural models. Complex quasi-toponyms are constructions with virtually unlimited number of variations. Complex quasi-toponyms can contain fictitious agionyms (models X-article-Y, X-sur-Y) and hydronyms (model X-sur-Y). The latter are mostly real, less often – fictitious. Quasi-toponyms of French-speaking Switzerland are marked by the influences of the German language and often correspond to the model X derriere la lune. Semantically, quasi-toponyms often contain components that implicitly refer to rural realities, emphasizing poor infrastructure, general piety, and lack of entertainment. In some quasi-toponyms, remoteness and hopelessness are expressed explicitly, although somewhat masked graphically. A number of quasi-toponyms are limited in use, related to historical phenomena, or have evolved from poetonyms through transonymization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".