Centre for Research on Globalisation
Bibliographic record
Abstract
Trade and Labour Economics Perspectives We may not be living in the age of mass migration, but we are surely living in an age of mass migration. 1 From 1965 through 1990 a fairly constant 2.2 % of the world population have been migrants. 2 However, this has involved an increasing rate of change to keep pace with the growing world population: the stock of migrants grew at 1.2 % from 1965-1975; 2.2 % from 1975-1985; and 2.6 % from 1985-1990. More importantly, for the purposes of this paper, relative to regional population, the share of migrants in the US and Canada rose from 6 % in 1965 to 8.6% in 1990 (with the greatest growth in the 1980s and 1990s); while the share in Western Europe rose 3.6 % to 6.1 % over the same period. This period has also seen a substantial shift toward developing countries as source countries for this migration: in the United States this share rose from 42 % in 1960-1964 to over 80 % in the 1980s and 1990s; in Canada this share rose from 12% to over 70%; while this share in Australia rose from 7 % to over 70%. In the 1990s, Germany and Austria experienced very large flows from Eastern Europe as well. As is well known, this period of rising immigration of unskilled workers coincides with a period of strong deterioration of the relative (and possibly the real) return to native unskilled
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.413 | 0.141 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".