Demoralization-led migration in Bangladesh: A sense of insecurity-based decision-making model
Bibliographic record
Abstract
Political hostility, unrest and flawed governance cause insecurity leading to demoralization, which triggers migration. There is a large body of literature on the determinants of international migration that highlights a range of factors to explain the direction and strength of migrant flows. For this research we interviewed 32 respondents who were a control group in a study conducted a decade ago. These respondents were determined not to migrate, but their migration decision was reversed over a period of 10 years. This article explores the relation between a sense of insecurity and the demoralization that influences migration decisions. It further investigates the causes that contributed to this change. As democracy shrinks, authoritarianism expands, implying that there is no accountability. This leads a country to widespread corruption, creating severe social injustices. People in general become demoralized and decide to migrate out. This article adds to the body of work by focusing on whether the migration decision is a response to widespread corruption, prevailing political conditions, violence, conflict, poor governance, an absence of rule of law and freedom or declining of democratic space in Bangladesh.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".