Dual citizenship in the context of globalization, identity and belonging debate
Bibliographic record
Abstract
At the beginning of the 21st century, there seems to be a global shift in paradigms of identity and belonging. For a long time, both of these entities have been deemed to be fixed and one-dimensional, tied to a specific nation, state and territory. But, under the influence of globalization, notions of identity and belonging are undergoing some fundamental changes. In the interconnected and migratory world we are living in, transnational communities possess and nurture identities of multiple belonging. These global interconnections create new challenges for previous notions of exclusive belonging to a single state-territory, and by extension, citizenship as the ultimate form of political belonging to a nation-state. In this context, dual citizenship has emerged as a legal recognition of this situation. In this paper, I discuss various issues connected to dual citizenship and argue for the need for recognition of full dual citizenship by every country of the world.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.070 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".