Bibliographic record
Abstract
A B ST R AC T. Across the globe, countries are promoting strategic or expedited passport grants, whereby membership is invested in exceptionally talented individuals with the expectation of receiving a return: for Olympic recruits, this means medals.The spread of the talent-forcitizenship exchange, with "Olympic citizenship" as its apex, is one of the most significant innovations in citizenship practice in the past few decades.In this emerging competitive environment, countries have come to realize that their exclusive control over the assignment of membership goods is a major draw.This realization has turned citizenship itself into an important recruiting tool.The Olympic citizenship dynamic highlights the growing influence of the economic language of human capital accretion in shaping targeted recruitment policies that are designed to attract top performers, whether in the sciences, arts, or athletics.In the process, it is our very understanding of citizenship that is undergoing a radical alteration.This Feature explores the analytical, normative, and comparative dimensions of Olympic citizenship, identifying the major players and interests at stake, assessing the national and international implications of such profound transformations, and highlighting the dark underbelly to the rise in Olympic citizenship grants.It concludes by developing possible new ways to address the challenges that Olympic citizenship creates, including proposed transnational responses to ameliorate concerns about exploitation and the unearned advantages that attach to the unregulated practice of cross-border talent poaching in pursuit of national glory.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 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".