Bibliographic record
Abstract
The term ‘intellectual migration’ initially referred to the exodus of European scientists and other professionals to the U.S. in the first half of the twentieth century. Academic and policy debates around issues of ‘brain drain’, ‘brain gain’, and ‘brain circulation’ in recent decades have intensified the usage of this term. A 2015 paper first attempted conceptualising Intellectual Migration as an analytical framework that encompasses a migration spectrum where students to professionals at different life stages move for intellectual pursuits that can advance career development. Li et al. (2021) articulate the framework by elaborating on the underlying key concepts – intellectual capital, intellectual nodes, intellectual gateways, intellectual peripheries – and the role they play in one’s spatial and social mobilities, and connecting internal migration with international migration. This special issue assembles empirical research that addresses issues like the (un)certainty of engaging in intellectual migration, agency-structure dynamics behind migration decisions, and the value of intellectual capital in the migration process. This introductory piece traces the evolution of the intellectual migration conceptualisation while synthesising the findings to affirm the usefulness of the framework in analysing higher-education and highly-skilled migration.
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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.017 |
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".