THE THEME OF CHALLENGES AND DIFFICULTIES FACED BY INDIAN IMMIGRANTS IN CANADA IN RUPI KAUR'S POEM BROKEN ENGLISH (2022)
Bibliographic record
Abstract
The research focuses on the challenges and difficulties faced by Indian immigrants in Canada, as portrayed in the poem ‘Broken English’ by Rupi Kaur. The study analyzes the intrinsic elements of the poem, including theme, feeling, message, diction, rhyme and rhythm, typography, imagery, symbol, and figurative language, along with the extrinsic elements of biography and meaning. Qualitative methods are employed to analyze data on Indian immigrant issues. The expressive approach is utilized for analyzing poetry and implicit meaning. According to research, Rupi Kaur’s “Broken English” revolves around the challenges faced by Indian immigrants in Canada. The second generation of Indian immigrants encounter a range of difficulties in adjusting to a new environment, including economic hardship and discrimination due to their non-native English proficiency. They also experience the consequences of discrimination that their parents faced, such as being denied recognition as Indian immigrants in Canada due to their thick accents. Furthermore, some second-generation Indian immigrants may discriminate against their fellow Indian immigrants.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.039 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".