Transnational Citizenship Capacity-Building: Moving the Conversation in New Directions
Bibliographic record
Abstract
Challenging statist understandings of citizenship neglectful of their own ironies, we explore the literature on circulation to argue that political actors build citizenship capacities through the transfer of various technologies, ideas, and modes of organization and by enhancing self-understanding across and within borders. This work is largely conceptual. Although we focus on transnational activist engagement with and within the Middle East, the theoretical linkages we make here can be extended to other social and political actors that operate within and across multiple geographical locales. To make our case, we briefly examine the importance of transnational circulation for citizenship capacity-building through a review of the relevant literature and then discuss how theories related to liminality and rhizomatic action can move the theoretical discussion in new directions. Our central argument is that the circular flow of people, political ideas, and tools across nation-state borders—including activists’ affinities, identifications, loyalties, animosities, and hostilities—are transforming contemporary social and political relations, including how people see themselves as citizens and build civic capacities in others. Political actors who act purposefully in various sites and scales of struggle are transforming how political subjectivity and citizenship are negotiated, claimed, justified, and legitimated regardless of citizenship status.
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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 | 0.043 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.093 |
| Scholarly communication | 0.026 | 0.064 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".