Bibliographic record
Abstract
According to the last head count in 1981 the total population ofPakistan was 83.782 millions. In addition, “there are two millionoverseas Pakistanis, more than one million left behind families and halfa million returnee migrants.” Let us assume that out of the 1.5 millionPakistanis who are working abroad at agiven time, 33.3 percent have anaverage of three dependents with them overseas. This brings the totalnumber of persons of Pakistani origin and their offspring abroad tothree million.It is estimated that over two-thirds of the working Pakistanis abroadare in the Middle East, especially Saudi Arabia, UAE, Libya, Kuwaitand Iraq. The rest of the total are working all over the world. After theMiddle East, their largest concentration points are England, the U.S.A.,Canada and Germany. Among the other countries with relativelysmaller-but significant numbers-of persons of Pakistani origin areBahrain, Oman, Qatar, Denmark, Singapore, Nigeria, Kenya, HongKong and Malaysia.Today Pakistan is one of the leading countries in exporting itsmanpower to the rest of the world. It is not an exaggeration of facts to saythat the foreign exchange sent home by the overseas Pakistanis iskeeping Pakistan afloat economically in these uncertain times. In 1983,close to three billion dollars were sent by overseas Pakistanis to theircountry. This is, again, one of the largest amounts sent by workers ...
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".