Bibliographic record
Abstract
Around the world there is a heightened interest in citizenship policy in the broadest sense — in the policy domains of education, naturalization and integration. We are witnessing widespread contestations over conceptions of citizenship, whether it be, for example, the challenges posed by multicultural diversity as a result of large-scale immigration in Western contexts, or those of the ongoing uprisings in the Arab world, as seen through the lens of the ‘Arab Spring’. Increasingly, we are observing governmental constructions of ‘common’ national citizenship in the context of perceived internal division — including devolution, increased social pluralism, immigration, increased ethnic and religious diversity and even civil conflict. However, these are simultaneously contested by students and teachers, as well as by prospective new citizens. This is the case not only in longer-established Western democracies but also in the new democratic states of Eastern and Central Europe, as well as in countries of the Middle East and Far East. This book draws on case study examples from an interdisciplinary perspective, including Belgium, Canada, Denmark, France, Germany, Ukraine, the UK and Palestinians in Lebanon, examining these contestations of citizenship in the domains of education and naturalization.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.311 | 0.168 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".