Unnoticed Ways in which Migration Reinforces Under development
Bibliographic record
Abstract
This paper analyses how migration from low income economies to high income economies can actually have negative rather than positive consequences on development and low income economies. There are noticed ways - such as brain drain, separation from family, etc – in which migration negatively affects sending- countries. But this paper argues that there are unnoticed ways in which migration negatively affects development and low income economies. For this paper, the relationship between migration and underdevelopment is a complex rather than a simple one. It is simple to see how underdevelopment causes migration. But to see that migration in turn reinforces underdevelopment, one needs to engage in some complex analysis. Alarmingly, the end-result of the complex analysis is that migration and remittances, while alleviating poverty, can actually make: citizens neglect their duty to hold their governments responsible for underdevelopment; and governments neglect their duty to find long term solutions to the problem of underdevelopment. In terms of methodological approach, admittedly this sort of work often requires empirical methods. Nevertheless, the aim of this paper is to do a theoretical analysis which will serve as the foundation on which future empirical works can be based.Key words; Institutions, Migration, Poverty, Remittances, Underdevelopment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".