Bibliographic record
Abstract
Two kinds of different culture have reflected the difference between two countries or two nation`s life attitude, way of thinking and life style. Though both China and French have long cultural history, but when regarding the sub discipline of wine culture under comparative culture, there are still lots of differences between the two countries due to different historical origin, regional characteristic, philosophy and cultural psychology. No matter from the wine itself or among the reflected wine culture related to the social aspects with drinking, these all reflect people's love of the good wine, which can also be detected from the China's traditional cultural atmosphere and France's romantic cultural feelings at the same time. Key words: system, the system theory, character, character structure Resume: Deux cultures differentes peuvent refleter les differences de l’attitude de vie, de la facon de pensee et des modes de vie de deux pays ou de deux nations. La Chine et la France sont deux pays d’une histoire longue et d’une culture brillante. Pourtant a cause de l’origine historique differente, des particularites territoriales, de la philosophie et psychologie culturelle differentes, il existe beaucoup de differences dans “ culture de vin“, une sous-discipline de la culture comparee, de ces deux pays. Du point de vue de vin lui-meme ou des aspects de la vie sociale lies au vin, on peut voir la passion cummune des deux peuples pour le vin et avoir une petite idee sur la culture traditionnelle de la Chine et l’atmosphere romantique de la culture francaise. Mots-cles: culture de vin, methodes comparatives 摘要: 兩種不同文化體現了兩個國家或兩個民族生活態度、思維方法與生活方式的差別。同是擁有悠久文化歷史的中法兩國，在比較文化下的子學科“酒文化”方面，又由於歷史淵源、地域特性及哲學與文化心理不同而存在著許多差異。無論從酒種本身，以及與飲酒有關的社會生活等方面所體現的酒文化中，體現了人們對美酒的熱愛這一共性，同時中國傳統文化氛圍與法國浪漫主義文化情懷也可從中窺見一斑。 關鍵詞: 酒文化；比較方法
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".