Cross-Cultural Scenario in Margaret Atwood’s Surfacing and Bharati Mukherjee’s Jasmine
Bibliographic record
Abstract
There is hardly a country in this industrialized world today, where one can find an ethnically homogenous population. The aftermath of colonialism, the creation of refugees- often the result of ethnic conflicts- and the movement of people in search of greater economic, political or social opportunities have contributed to the worldwide mix of people. Canada and India are the countries affected by the growing diversity. However this diversity has different facets in both the countries. In the literary world Canada, Multiculturalism is the main theme of writing and in India, presentation of cultural diversity is yet at the beginning stage. This statement has to be tasted on the fictional works of Margaret Atwood from Canada and Bharati Mukherjee from India. Both the writers are very unique in their writing and have trodden the different ways of using Cultural-diversity. Culture is an integral part of a human society and its nation. Then the question arises: what is culture? The Oxford English Dictionary defines culture as a “particular form or type of intellectual development in a society generated by its distinctive customs, achievements and outlook.” At the wide canvass, culture is taken as consolidating the way of life of an entire society and includes codes of manners, dress, language, rituals, social customs and folklore of a nation. Every country has a typical and distinctive culture of its own. However, when an independent country becomes a colony, the native culture goes under a change. This is the case with the countries like Kenya, Nigeria and India. When these countries came in contact with western culture, a process of change in culture was initiated, and this journey made the traditional culture of respective countries destroyed. While Indian literature had cross cultural encounters with the English studies, Canada has been undergoing a cultural metamorphosis with the mix of second races and people from all over the world.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.059 | 0.016 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 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".