The Oriental Journey -A Trajectory in Chitra Banerjee Divakaruni’s The Mistress of Spices
Bibliographic record
Abstract
This article is an attempt to explore the prospects of transitioning from alienation to acculturation. The ability to change the bitterness of alienation into the sweetness of acculturation lies in the hands of the immigrant. The Mistress of spices through the characters in the novel divulges the secret of acculturation along with its hitches and the requisite for acculturation. Having made it to the host land, it is good for the immigrant to stop brooding over situations and work on settling down. At times, the immigrant might have to resort to the road less travelled, as suggested by Robert Frost in his poem, The Road Not Taken, “Two roads diverged in a wood, and I—I took the one less travelled by, And that has made all the difference”.The diasporic phase should no longer be a battle of the minds but rather a platform for a growth mindset. As the shifting of tectonic plates creates new territories and new horizons, it is high time that a shift in the diasporic mindset is observed. One of the unique features of a growth mindset is to use the roadblocks of migration as opportunities for learning. Migration is a good time to understand and experience the wholesome culture of the place, to dissect the experiences of myth and misconception and fact from fiction, firsthand, in tandem with the sharing of cultural best practices and customs of the homeland.
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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 | 0.001 |
| Science and technology studies | 0.033 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".