СПЕЦИФІКА СМАКОВИХ ОБРАЗІВ ІНШОГО В ПОВІСТІ РОЗЕЛЛЬ ЛІМ "МАГІЧНА ЧАЙНА КРАМНИЧКА ВАНЕССИ Ю В ПАРИЖІ"
Bibliographic record
Abstract
This paper makes an attempt to analyze the process of artistic modeling taste images of the Other with the help of sensory sensations of the characters in the story “Vanessa Yu’s Magical Paris Tea Shop” by the Canadian writer of Asian origin Roselle Lim through the prism of gustative poetics. Special attention is paid to the process of artistic reflection and presentation of taste sensations of the characters as important carriers of information about taste images of the Other. The analyzed story by Roselle Lim is a work of art, which demonstrates the world of taste sensations with the representatives of different nations: the French, the Chinese, and the English. It should be noted that the author of the story “Vanessa Yu’s Magical Paris Tea Shop” describes the culture and traditions of the Other precisely through the taste perception by the characters. Gourmet preferences are decisive in the fate of the characters, and their taste sensations are emphasized throughout the whole literary work. It is the taste sensations of the characters that perform compositional functions in the unfolding process of the plot in the story. Food preferences of the characters are depicted as unique procedures that reveal their inner world, characters, promote communication between representatives of different nations. The taste perception has powerful communicative functions, as in order to know the taste of the French cuisine the author gathers her characters in the heart of France – Paris. The uniqueness of Roselle Lim’s story lies in its multifaceted manner. The writer presents an artistic image of taste and builds the plot of a work in such a way that the specified image becomes multi-functional in revealing the characters’ traits and their psycho-emotional states. This image performs important unifying, aesthetic, characteristic and informational functions in the story. The writer presents the perception of taste by representatives of different nations (Canadians, the French, the Chinese), globalizing the taste sensations of the characters as much as possible.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".