Canada Research Chair in Citizenship and Governance Supranational citizenship-building and the UN. What can we learn from the European experience?
Bibliographic record
Abstract
Work in progress – Do not quote without permission of the author. The word “supranational citizenship ” or “UN citizenship ” is not yet part of the United Nations ’ usual vocabulary. The use of the “citizenship ” concept in UN discourse is quasi-exclusively limited to the national context, a definition of citizenship bounded by state borders (Delcourt, 2006: 187). Must we therefore conclude that the UN is not “making citizenship ” at all? Given that the notions of “supranational ” or “UN citizenship ” are absent from the United Nations ’ official discourse, the answer seems obviously to be YES. Yet, consideration of the European experience demonstrates that this response may be too hasty. The example of the EU, and some work on European citizenship, suggest another answer to this question. The aim of the present paper is to show that, just as the European Union was making citizenship well before the Maastricht Treaty mentioned European citizenship, the United Nations system is a supranational framework that is beginning to engage a process of citizenisation. Based on a large and dynamic conception of citizenship, defined as a double
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".