Bengali immigrants and renovation of the administrative structure of princely state Tripura: An analytical study
Bibliographic record
Abstract
Movements of immigrants were not a new phenomenon for the state Tripura. Tripura began to witness the flow of immigrants’ right from the last quarter of the 19th century and before her amalgamation, she witnessed a large number of immigrants especially from Colonial Bengal. The close affinity between the Colonial Bengal and Hill Tipperah caused the movements of the people from one place to another. Whether the rulers of Tripura whole-heartedly wanted to make a close affinity with Colonial Bengal is a matter of long discussion, but it is true that they had to admit the Bengalis into their territory. Now what were the reasons for which the rulers of Tripura pulled the Bengali immigrants especially from the educated sections from their state of origin? The present study is made to find out analytically the reasons behind those educated Bengali immigrants who immigrated into the Princely state of Tripura.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".