Rashid Amjad (ed.) The Pakistani Diaspora: Corridors of Opportunity and Uncertainty. Lahore, Pakistan: Lahore School of Economics. 2017. 337 pages.
Bibliographic record
Abstract
The book “The Pakistani Diaspora, Corridors of Opportunity and Uncertainty”, which is edited by Rashid Amjad, is a collection of 17 academic essays on Pakistani migrants and Pakistani diaspora in different countries. This book presents diverse viewpoints in the study of diaspora. This book does not just analyse the size of the diaspora in a chronological manner, but it also provides important understanding of the cost and benefits associated with migration and assimilation of the migrants’ families in new environments. In the first paper, the author tries to capture the salient features and dynamics of Pakistan’s “age of migration” across home and host countries. By 2017, the estimated diaspora was at 9.1 million – almost 5 per cent of Pakistan’s population. The labour class started to migrate to the UK in 1950s while highly skilled professionals started moving to the US and Canada in 1960s. The unskilled and semiskilled workers began to move to the Middle East in 1970s and due to easing off their visa policies in 1990s, migrants began moving to Europe, Singapore, Thailand, Malaysia and Australia from Pakistan. According to the author “A large number of people face losses in the struggle to migrate to foreign countries. A majority of illegal migrants are imprisoned in different countries while trying to reach Europe while dozens are killed on their way to Greece.”
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".