Precedent Onyms in Religious Phraseological Units in the Spanish Language (On the Transition from the Phraseological Units with Precedent Onyms to the Precedent Phenomenon)
Bibliographic record
Abstract
The article deals with the matter of precedent onyms in religios phraseological units in the Spanish language. The task of the given article is to look through the samples on the given phenomenon.Religious phraseological units in the Spanish language are analyzed from lexical and componential points of view.The linguistic or the semantic aspect of cognitivism, i.e. the mental identity of a native speaker can be distinctly observed in the phraseological units of the Spanish language. So, the evaluation of religious phraseological units of the Spanish language from the prism of new analysis of cognitive semantics suggests that native speakers enjoy the privilege of transmitting information through certain word-codes and very often there is a certain parallelism, more precisely, commonness with cognate languages, particularly, with the languages of peoples of the same religion.The religious phraseological units have meanings different from their initial semantic capacity due to the semantic extension in the succeeding phrases, and these meanings are consistent with linguo-pragmatic condition of the media discourse in which a religious phraseological unit is practiced.In the Spanish political, economic and media discourse, the religious phraseological units such as El Festín de Baltasar have gained additional connotations through semantic reassessment, and it is possible to conclude that this connotation has not only situational and contextual essence but also has acquired wide and general usage rather than occasional.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".