A methodological analysis of national models of integration: time to think without the models?
Bibliographic record
Abstract
National models of integration are widely used to understand the relationship between nationalism and integration-immigration policies. In this methodological article, we highlight two key concerns. First, national models of integration emerged out of inductive and normative case studies. The analytical value of models which are based on inductive and normative reasoning is not directly generalisable beyond very similar cases. Second, and despite their inductive limitations, the generalisation and application of national models beyond these analytical limits have produced tautological and essentialist outcomes – fitting empirical data into the models and overlooking other possible correlations between nationalism and integration policies. Amid recent scholarly attempts at amending the national models, the question remains whether national models are truly useful tools for the comparative studies of the relationships between nationalism and integration-immigration policies. We argue that comparative examinations require a more robust theoretical and methodological approach that can be used across periods and contexts without becoming tautological and essentialist.
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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 | 0.070 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.010 | 0.029 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".