CUISINE AS A MARKER OF CULTURAL IDENTITY. A HERMENEUTIC LOOK AT "INDIAN CUISINE", A SHORT STORY BY TRINIDADIAN-CANADIAN WRITER RAMABAI ESPINET
Bibliographic record
Abstract
When it comes to cultural identity, cuisine is generally overlooked or relegated. Emphasis is rather made on history, religion or language (the so-called deep structure of a culture). Notwithstanding, one´s vernacular cuisine outlives one´s vernacular language in generations of immigrants. No matter how much it changes as compared to the original food of their homeland, immigrants and their descendants “keep cooking and eating some version of the family´s ‘mother cuisine’ ”. Immigrants’ cultural identity is embodied in what has been called “ethnic food”, which is in turn caused by several factors. The author of this paper examines cuisine as a marker of cultural identity and, for that purpose, he gives a hermeneutic look at the short story “Indian Cuisine”, by adhering to Daniel Chandler´s typology of codes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".