Bibliographic record
Abstract
The carnival has always been aimed at getting emotional pleasure by acting and theatricalization. When organizing carnival tours one should take into account a number of tourists` requirements:hotel location – near the place where carnival events are taking place;presence of restaurants and quality food;hotel architecture ancient style;room interior and colour spectrum;a number of service staff;presence of shops or rentals of carnival costumes.The most popular South-American carnival is the one in Rio de Janeiro where only the tribunes of specially built dance floor accommodate over 850000 spectators. The biggest carnivals in North America are "West Indian carnival" in New York (3 million), "Caribbean carnival" in Toronto (1,5 million), winter carnival in St. Paul (USA).In Europe most of tourists are attracted by carnivals in London (up to 2 million people), Cologne (over 1,5 million people), Berlin (carnival of world cultures – about 1,5 million people), Nice (over 1 million people). Venetian, Roman, Valencia, Avignon, Lyons, Cologne and Nuremberg are considered the biggest in Europe. There are large carnivals in Africa (RSA, Angola), South Eastern Asia, and Australia.There are several reasons for carnivals popularity. First, any festivity is an effective recreation and people used the opportunity to rest after everyday work. Second, the carnival of later Middle Ages and the Renaissance was a specific protest against the church and royal power as well as the official culture. Third, the carnival is a holiday of permissiveness when a person ignored limitations and bans of the everyday life and could meet most of his/her physiologic and spiritual needs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.018 |
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".