Parties’ Platforms, Migration, and Security: Patterns and Determinants
Bibliographic record
Abstract
The integration of national security concerns in states’ treatment of international migration has become a prominent issue in recent years as global migration is regularly presented as a security threat to host countries. In this paper, we illuminate a number of patterns and determinants of political parties’ treatment of immigration as a security matter–the securitisation of migration–through the comparative study of a new data set of parties’ platforms in Australia, Canada, the United Kingdom, and the United States that spans seven decades. We distinguish between the salience of migration and of security in parties’ platforms and explore the effect of various migration-related domestic-level factors in accounting for both phenomena. Our empirical strategy to assess the extent to which political parties have presented, promoted, and framed the security concerns associated with migration is based on textual data from parties’ platforms. We report three findings. First, parties have increasingly mobilised migration in their platforms regardless of ideological or partisan commitments since the 1990s, and the number of refugees hosted by a country constitutes the primary migration-related explanatory factor for this fact. Second, we show that mainstream right-wing parties account for the increase in the securitisation of migration in parties’ platforms. Finally, among migration-related domestic factors, the number of refugees has (surprisingly) a negative impact on the securitisation of migration in platforms. Overall, this paper illuminates important patterns in how parties discursively link security and migration, and investigates the determinants of this widespread phenomenon.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".