Причины возникновения и особенности формирования квебекских ругательств (les sacres)
Bibliographic record
Abstract
Fedor S. Nepsha, Tatyana L. Bogatyreva (Kemerovo, Russian Federation) The paper examines the main reasons for the emergence and spread of Quebec swearing (les sacres). Socio-psychological aspects of their origin and distribution in the Quebec dialect of the French language are analyzed. It is established that after the Silent Revolution (Revolution tranquille) of the 6070s of the 20th century, the influence of the Catholic Church in Quebec was minimized, so the “les sacres” lost their original meaning and no longer bear a religious connotation. Various variants of classification of mechanisms of word-formation “les sacres” are also given in the work. Separately, the classification proposed by Andre Bugaev has been singled out, within the framework of which the mechanisms of the word-formation “les sacres” are discussed in detail. With its use, it is suggested to perform a linguistic analysis of the retrospective of the word-formation system “les sacres” and to establish a relationship with socio-political factors. Key words: Quebec swearing, Quebec French, word formation, les sacres, Catholicism, Francophonie. DOI 10.23683/1995-0640-2017-4-116-124 Quote : Nepsha, F. S. & Bogatyreva, T. L. (2017). Reasons of Emergence and Formation Features of Quebec Swearing (Les Sacres). Proceedings of Southern federal university. Philology . 4, 116‒124.
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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 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".