An Analysis of Male Internal Migration and Its Correlation toEmployment Status: Evidence from the Punjab
Bibliographic record
Abstract
Migration plays a pivotal role in the reallocation of human resources under changing demand and supply conditions. Migration takes place when an individual decides that it is preferable to move rather than to stay and where the difficulties of moving seem to be less than the expected rewards. In recent years there has been a trend of increasing migration rates. The United Nations (2000) estimates that about 140 million persons (roughly 2 per cent of the world’s population) reside in a country where they are not born.1 Usually migration takes place from the regions that are associated with poverty and insecurity towards regions which offer greater security of life, employment and basic social services. Poverty pushes people to migrate to urban areas-the outcome, the world’s urban population approaches 2.3 billion by 1990 with 61 per cent living in the metropolitan areas of developing countries and touches 66 per cent in 2000 (United Nations). Within the world Asia has about 15 of the largest cities of the world and most of them are growing at more than 5 per cent per annum. Increased rate of natural growth, immigration and rural-urban migration might be the causes of such a high rate of growth of urban population.
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 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 | 0.000 |
| 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".