Proceedings of the International Conference on Onomastics ”Name and Naming”
Bibliographic record
Abstract
Art, in general, and painting, in particular, offer a large variety of names.Like any artistic product, a painting is identified through its title.If the act of naming persons is manifested through baptism, the naming of paintings is illustrated through the act of giving titles.The title, which is necessary from the moment of introducing a painting to the public, is the name of an artistic depiction.The sacred is defined in painting through religious representations.Names of paintings are no more than descriptions of the painted images, of their biblical themes and symbols.The field of our study is represented by onomastics.Our approach is mainly linguistic (semiotic, semantic, pragmatic, lexical, and grammatical), considering, at the same time, the psychological and sociocultural factors which influence the naming of paintings (the artistic movement, the society in which an artist lives, his/her view upon life and/or divinity, personal experiences).The aim of this study is to present and analyse some aspects of sacredness occurring in names of paintings, as well as the structure of these onymic systems.The corpus is composed of titles taken from art albums and books or from the Internet (websites of museums and art websites).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 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".