The Politics of Land Law: Poverty and Land Legislation in Bangladesh
Bibliographic record
Abstract
This thesis examines the major colonial and post-colonial land laws of Bangladesh and their relationship with poverty. It interprets them in the light of historical developments and social realities. The thesis argues that land laws in Bangladesh are essentially anti-poor. They contribute to the perpetuation of poverty. At present, two-thirds of the poor in Bangladesh are land-related poor. The land system that prevailed in colonial Bengal during the British period deprived the peasants of their land rights. This situation demanded a radical land reform based on a distributive approach upon decolonisation in 1947. Unfortunately, in the post-colonial political and legal settings of Bangladesh, land distribution has been unequal. Such inequality coupled with a weak land tenure system and fragile institutional reform created widespread poverty. The Bangladeshi land laws are complex and vague and dominated by politics. Its land law regime has structural loopholes and ideological drawbacks, which are enough to make reform attempts dysfunctional. Poverty in Bangladesh is a result of cumulative and mutually reinforcing deprivations. Land law is a major participant in it. Poverty will persist unless law addresses the true reasons of the poverty and a pro-poor approach to land reform is pursued. The gap between “law” and “land” is exposed and a distributive land law reform model is proposed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".