Bibliographic record
Abstract
age and experience 214, 229 age, marginal effect of close to zero, non-significant 184 all year-to-year labour-market dynamics among recent highereducation graduates relative to time of graduation 97 amenity variables effect on migration flows 156 American Community Survey for 2011 115 applied sciences graduates poor match between education and job after graduation 118 Augmented Inverse Probability Weighting (AIPW) 22 Australia domestically educated overseas graduates (DEOGs) 11-12 Australian and New Zealand Standard Classification of Occupations 2006 (ANZSCO) 16 Australian Statistical Geography Standard Statistical Area Level 4 (SA4) 15 average marginal effects on migration propensity by study region 129 average marginal effects on subsequent migration propensity by study region and prior migration status 131 benefits from migration for highly educated individuals 205 birth in city, graduate advantage 189, 192 Boston, Philadelphia, San Francisco, Los Angeles, large economic hubs 209 brain-drain 4, 175, 205 'brain gain' 4 Brazil, Latin America size and number of migrants 175 Canada large urban areas persons with university degrees 42 migration, education, employment 43-45 Catalan PhD holders migration decisions 164-170 Catalan university graduates migration decisions 164-170 Catalan University Quality Assurance Agency (AQU) 164 Catalonia, Spain 164-170 certificate and diploma holders, Canada 50 characteristics of the flows 145 child migration little effect on college graduation, Mexico 192 positive effect on migration in later life 184 climate characteristics 145, 151-154 cognitive skills literacy, numeracy, problem-solving, communication skills 17 Colombia, Latin America internal migration, human capital 175 commune, smallest administrative area in France 143 competencies/skills utilisation, Italy 63 compulsory education, then high school in Finland 119 computer-assisted telephone interview (CATI), Spain 166 consumer price index, Finland 122 contemporary trade-offs 108
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.659 | 0.522 |
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".