Bibliographic record
Abstract
Abstract Since the very existence of this life-sustaining planet Earth, be it the search for food or a new water resource or a place fit for shelter to sustain life, almost every living being has had to travel hundreds and thousands of miles when needed. This book is about the world’s biggest migration in human history which was not a choice of those who migrated! After a long period of struggle for independence when a nation fought for its freedom to liberate itself from the shackles of slavery, little did they know the cost to be paid would come in the form of a new pair of chains… the partition, the scars of which are remnant till date. The partition of 1947 claimed the lives in millions and affected countless families which got burnt in the fire of communal violence. This book is an effort to acknowledge the facts which were collected from the survivors who suffered the atrocities of times and still could muster the courage to rise again. This book has 47 such stories covering 52 cities, across 5 countries, with sole aim of bringing forward the real-life experiences of the people who faced partition, who still feel it should be the harmony and peace that one must seek in their respective story over violent behaviour of mankind in desperate times.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.645 | 0.427 |
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".