Navigating the Vulnerable: Threats of National Security to the Global Sanctuary Effort
Bibliographic record
Abstract
For the purposes of this paper, I will research how global citizenship principles conflict with self-interested states and their priority of national security in the context of sanctuary policies. This research paper will serve to address the nexus of global citizenship and sanctuary policies within the framework of national security and its virtues. Whereas good global citizenship assumes unrestrained sanctuary, the perceptibility of this is low given the rise in influence of dominant international relations (IR) theories of rationalism, realism, and liberalism which work to guide the behaviour of self-interested states. For this reason, it is apparent that states stress territorial integrity and the greater concern over national security rather than ‘performing’ good global citizenship by operating sanctuary cities. Indeed, many states do host formal sanctuary cities, however, the research presented will argue why and how state governments fail to defend the securitization of the very vulnerable populations they sought to protect. Particular focus will be allocated to asylum-seekers and refugees. No single social or political theory is able to justify the reasoning behind states’ noncompliance with global citizenship and sanctuary principles as state decisions of deportation are complex. In relief of this, several independent variables, including; ethnic and cultural identity, national citizenship, level of education, and presumed level of security concern will be operationalized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".