Deciphering Tribal Migration through the Pages of Contemporary Literary Narratives in Translation
Bibliographic record
Abstract
Migration, encompassing both international and internal movements, is a multifaceted and pervasive global phenomenon with social, economic and cultural implications. The internal migration of tribal population in India is one such movement that opens up avenues of discussion regarding the socio-cultural, economic and political impact of migration. Tribal migration currently in India, is propelled by the complex interplay of push and pull factors. The proposed paper intends to conduct a comprehensive analysis of tribal migration with the help of literary narratives written in regional literature translated to English. The study situates the selected texts in the framework of migration studies to examine the dynamics of tribal migration in India. While the existing studies problematize the event of tribal migration, the current examination endeavours to broaden its scope by analyzing both the challenges and benefits of migration for the tribal population in India. The study underscores that migration for education empowers the younger generation of tribal population from their disempowerment and marginalisation, owing to the fact that it would enable intercultural transactions and social exposure. Further, it foregrounds the tribal actuality of forced migration that ultimately accentuate their existing deprivation. An understanding of the aforementioned dual dynamics of tribal migration would enhance the contemporary social policies aiming at tribal empowerement.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".