Language testing, migration and citizenship : cross-national perspectives on integration regimes
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
1. Introduction, Guus Extra, Max Spotti and Piet Van Avermaet (Tilburg University, Netherlands and Centre for Equity in Education, Flanders, Belgium) Part I: European countries 2. The politics of language and citizenship in the Baltic context, Gabrielle Hogan-Brun (University of Bristol, United Kingdom) 3. Language, migration and citizenship in Sweden, Lilian Nygren-Junkin (Goteborg University, Sweden) 4. Inventing English as convenient fiction: language testing regimes in the United Kingdom, Adrian Blackledge (University of Birmingham, United Kingdom) 5. Language, migration and citizenship in Germany Patrick Stevenson & Livia Schanze (University of Southampton, United Kingdom) 6. Language policies for citizenship and integration in Belgium, Piet Van Avermaet & Sara Gijsen (Centre for Equity in Education, Flanders, Belgium) 7. Testing regimes for newcomers to the Netherlands, Guus Extra & Max Spotti (Tilburg University, Netherlands) 8. Regimenting language, mobility and citizenship in Luxembourg, Kristine Horner (University of Leeds, United Kingdom) 9. Language, migration and citizenship in Spain, Dick Vigers & Clare Mar-Molinero (Southampton University, United Kingdom) Part II: Non-European countries 10. Language, migration and citizenship in the United States, Tammy Gales (University of California Davis) 11. Language, migration and citizenship in Canada, Lilian Nygren-Junkin (Goteborg University, Sweden) 12. The spectre of the Dictation Test: Language testing for immigration and citizenship in Australia, Tim McNamara (University of Melbourne, Australia) 13. Citizenship, language, and nationality in Israel, Elana Shohamy & Tzahi Kanza (Tel Aviv University, Israel Bibliography Index.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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