Mass Migrations And The Emerging Socio-Political Constructs Of Identity, Interest And Security In Hosts State
Bibliographic record
Abstract
This paper examines two distinct phenomena in the regulation of Migration and refugee hosting: First, host states' requisite socio-cultural and ethnopolitical receptivity, which is a prerequisite for reducing migration-related violence and instability. Here, the socio-cultural receptivity of the host state and transnational ties with groups travelling in disguise or posing as refugees and migrants may mitigate violence. Second, regarding socio-cultural similarities, political representation, and legal systems for integrating migrant and refugee communities, host countries have little in common with migrant groups. Hosting nationalistic identities and social security preferences may exacerbate the marginalization and polarisation of migrant and refugee populations. The materiality of security concerns, as opposed to the perception of a security threat, may manifest ironically in both cases, where states closer to migrants in terms of identity and value may be unable to anticipate the potential for socio-cultural fault lines to be exploited and transformed into ethnopolitical issues, border vacillation, and the spread of crime and terror nexuses from trans-national migration links. In contrast, states with minimal socio-cultural proximity to migrants may face reactive ethnic and political mobilization based on ethnicity & Nationalism against or within migrants. The subject addressed in this paper is: what social constructs of identity and interest perceptions may emerge from widespread cross-border movement, posing political and internal security concerns in the host state in the long run? References G Oscar & D Gasper. (2013). Human Security: A thematic guidance note for regional and national human development report teams. 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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".