Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The international mobility of people and migration flows are critically influenced by differences in per capita incomes, real wages, job opportunities, institutional capacities and living standards across nations and cities. Its dynamics are shaped by social networks and regulated by the migration policies of receiving countries. International migration represents around 3.3% of world’s population; up from 2.7% in 1995. It is composed mainly of working-age people, with men and women migrants being in roughly equal numbers. Historically, the globalization process of the late 19th and early 20th centuries was also accompanied by large migration flows, mostly, from the “Old World” (Europe) to the “New World” (United States, Canada, Argentina, Australia, and other countries in the Global South). Starting in the 1980s migration has increased relative to a rise in total population, although the share of international migration to total population was, on average, higher in the first wave of globalization of the 1870–1914 period. Main substantive topics and new themes in the field of international migration include: (a) the motivations and determinants of the international mobility of the wealthy (High-Net Worth Individuals, HNWIs), a largely unexplored topic in the literature of international migration; (b) the international migration of talent (high-skills, educated, and gifted people), (c) the linkages between the mobility of talent and the mobility of capital and their evolution over time affected by macro regimes and international conditions, (d) The relation between macroeconomic and financial crises (e.g., the 2008–2009 crisis), stagnation traps and immigration flows, (e) the influence of international migration on inequality within and between countries, and (f) forced migration, displaced population and humanitarian crises, following war, violence, persecution, and human rights violations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it