Strategic Migration Policy - HD.SMP 1.0 [research data]
Bibliographic record
Abstract
As part of the Strategic Migration Policy project, a dataset (HD.SMP 1.0) was developed that systematizes and codes bilateral migration agreements of the destination countries/regions of the European Union and its member states, Switzerland, Norway, the United States, Canada, New Zealand, Australia, and the United Kingdom between 1990-2022. The dataset includes 1457 agreements and conventions, which were recorded using categories that provide information on parties involved, legal status, scope, degree of informality, and degree of externalization. It excludes agreements aimed exclusively at regulating labor migration. --- Im Rahmen des Projektes „Strategische Migrationspolitik“ wurde ein Datensatz (HD.SMP 1.0) entwickelt, der bilaterale Migrationsabkommen der Zielländer/Regionen der Europäischen Union und ihrer Mitgliedsstaaten, der Schweiz, Norwegens, der Vereinigten Staaten, Kanadas, Neuseelands, Australiens sowie des Vereinigten Königreichs zwischen 1990-2022 systematisiert und kodiert. Der Datensatz beinhaltet 1457 Abkommen und Übereinkünfte, die anhand von Kategorien erfasst wurden und die Auskunft über beteiligte Parteien, rechtlichen Status, Umfang, Informalitäts- und Externalisierungsgrad geben. Er schließt Abkommen aus, die ausschließlich auf die Regelung von Arbeitsmigration abzielen.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.042 |
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".