Beyond open and closed borders: the grand transformation of citizenship
Bibliographic record
Abstract
The Jurisprudence Lecture, delivered by Ayelet Shachar, challenges the established dichotomy between open and closed borders, showing that one of the most remarkable developments of recent years is that borders are simultaneously both more open and more closed. Membership boundaries are not fixed or static. Instead, they expand or shrink, selectively and strategically, depending on the target populations they encounter. Moving beyond the open-closed binary, Shachar conceptualises a far more dynamic, multifaceted, and kaleidoscopic process, which we might call the grand transformation of citizenship. Drawing on a rich set of comparative examples, this article explores three intersecting yet analytically distinct dimensions of the realignment of citizenship: the territorial, the cultural, and the economic. This framework of analysis highlights the interconnected facets driving this transformation, and considers the puzzles that emerge when we think about them in tandem. The moving parts that together comprise this transformation generate novel strategic possibilities for the state, which in turn creates new latitudes for the few and new restrictions for the many. Shachar's goal, ambitious from the start, is to engage in theory-building by articulating the form and function of each of these facets of transformation. She further demonstrates how their variable combinations intermingle to police and restrict (or alternatively, relax and facilitate) access to membership in a globalising world, determining who may overcome the odds in the birthright lottery.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 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".