Bibliographic record
Abstract
People living all over the world belong to different religions, follow different cultures and speak different languages. If people of one nation go to another nation for their livelihood or education, they have to adapt themselves to the changing situations and places lest they should experience untold sufferings. Life throws all a lot of challenges, both simple and complicated, and it is up to all to rise and perform, take decisions that can be sometimes satisfying, and sometimes disturbing, and walk through it as if none were affected by it. It is not an easy thing to do. It is never easy to answer his heart as the questions surface and resurfaces time and again. Life is not a bed of roses to live easily. Lahiri is an Indian by birth but she has America as her permanent dwelling place. Hence, she has faced a lot of problems as an immigrant which she tries to show in her work. Hers are perfectly placed words lining themselves into elegant sentences whose subject matter: family, mothers and daughters, assimilation, alcoholism, children, marital love and touch us all.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.021 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".