Naming and multiculturalism in jokes: how do the Romanians perceive others?
Bibliographic record
Abstract
In addition to its primary, objective function of individual identification, anthroponyms can also fulfil a social function, which implies the inclusion of several entities in a class based on their sharing – due to their belonging to the same ethnolinguistic culture – similar beliefs, habits, customs etc. This paper aims at recording the main names that Romanians employ in jokes (taken from the local onomasticon or from the name stocks of various ethnicities as a result of direct or indirect contact) and to identify their pragmatic functions. The name forms analysed are culture-bound specifying markers, i.e. names that are considered representative of the peoples to which this investigation refers. The author examines first names in humorous discourse, particularly in jokes. These onyms tend to lose their individual character and appellativise by means of deonymisation, as they metonymically define an ethnic/national community. At the same time, there are certain name substitutes, i.e. generic appellatives which behave as signs that identify every denotatum in a given community. Thus, several conclusions can be drawn about the way in which the Romanian people expresses/experiences multiculturalism on the level of the understanding/interpretation of the names (name substitutes) analysed.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".