Introduction to the special issue of the International Journal of Comparative Sociology on “National identity, nationalism, patriotism, and globalization”
Bibliographic record
Abstract
This editors’ introduction into the themed issue of IJCS dedicated to the analysis of comparative survey work on national identity and globalization presents a very brief overview of core hypotheses from the five articles collected in the issue. The articles offer a variety of new, rather differentiated insights into how individual-level national identity attitudes and sibling concepts like belief in national superiority, patriotism, and nationalist chauvinism are related to societal-level variables that tend to vary with exposure to aspects of globalization, such as migrant influx and economic competition. Aside from the focus on those new contributions, the introduction also offers a few observations on the challenges that the wider national identity research field still faces. Given that the field is dealing with several overlapping attitude concepts, this centrally concerns a partial lack of conceptual clarity, which sometimes translates into ambiguous operationalizations and incomplete or imprecise explication of theoretical mechanisms. We conclude that the contributions of the themed issue, with their careful attention to particular aspects of measures and multi-level processes, may serve as another stepping stone for overcoming at least some of those challenges in the future.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".