Bibliographic record
Abstract
Displacement for survival, perhaps since the inception of life on the earth, has been a marked feature of the animal kingdom—be it birds, mammals, reptiles, or human beings. However, these are only human beings who migrate not just for survival but also for a better life. In this very context, the present paper deliberates on the migration of Punjabis to England, America, and Canada through some of the short stories in Punjabi produced by the migrants settled in these countries. The stories have been taken from an anthology titled Punjabi Parvasian Dian Kahanian (The Stories from Migrant Punjabis), edited by Jinder and Baldev Singh Baddan. The selected stories bring forth the diasporic people’s desires, sometimes lust also, to enjoy the riches and the glamorous life of the western countries and their struggles for success in foreign lands. This literary response is a collection of mixed experiences. On the one hand, it exhibits bewilderment at the incompatibility with the new culture, a sense of alienation, and the sacrifices of health and ethics to reach prosperity; on the other, it brings forth how the migrants learn to explore themselves, gain independence (especially women) and shed their weaknesses and narrow attitudes in the new liberal environments. This study also includes the problem of illegal migration, the vice of greed behind it, the resultant fear and frustration, and how it results in turning humans into not-less-than-beasts.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".