Where does all the talent flow? Migration of young graduates and nongraduates, Canada 1996–2001
Bibliographic record
Abstract
It has recently been suggested that cities and regions should focus upon attracting talented individuals as a means to develop. Such a suggestion implies two things: first that it is possible to meaningfully alter migration flows by way of local policy, and second that these flows have an effect on local growth (and not the reverse as has generally been admitted). In this paper, we begin to investigate the empirical foundations of such assertions by examining some structural determinants of graduate migration flows by comparing them, in a gravity model context, to flows of nongraduates. Our contention is that, if migration flows are structured by such factors, then policies aimed at modifying flows—and any research purporting to give such policy advice—must first take them into account. We show that migration flows are strongly dependent on basic gravity variables such as size and distance, but that these and other variables (such as income differences, presence of graduates and border effects) do not affect all flows equally. Furthermore, we show that certain factors that are assumed to be local (such as wage levels) in fact only operate at a provincial level. Thus policies implemented locally may have little or no effect if they are manipulating factors that operate at a different scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".