How Do Conceptions of Nationalism Influence Public Attitudes Towards Refugees?
Bibliographic record
Abstract
This dissertation examines how the conception of national identity influences public opinion on refugees. Classic scholarship on nationalism (see Gellner, Kohn, Greenfeld) has long argued that the conception of nation (i.e., what criteria constitute membership and citizenship) shapes the political behavior of individuals within the national community. Generally, many argue that individuals living in states with less inclusive conceptions of nationalism are less trusting of outsiders. In contrast, those living in a state with a more inclusive conception of nationalism are more trusting of outsiders. However, these claims have largely been based on sweeping generalizations based on historical case studies and have not been empirically tested. On the other hand, there has been a considerable amount of work that has examined attitudes towards refugees, but these studies have focused on individual characteristics that explain attitudes about refugees, such as partisan politics, social class, exposure to refugees, prejudice and fears, and social attitudes on social inclusion and exclusion. However, what has been missing is whether how a nation is conceived affects individual attitudes. Gellner (1983), for instance, argues that as part of the nation-building process, common social attitudes and ideas of “us and them” are ingrained by the national education system to build a national identity. This dissertation examines this longstanding proposition to investigate whether such national conceptions impact attitudes towards refugees. To investigate these effects, I begin by using Cetra and Swenden’s conceptualization of different types of national conceptions: dominant, integrationist, and composite nationalism. The dissertation is built around three empirical chapters. In the first empirical chapter, I hypothesize that individuals living in a composite nationalistic state will be more accepting of refugees compared to individuals living in a dominant nationalistic state. Using a cross-national Survey from the World Value Survey (WVS) (2017-2022), I find that the influence of state conceptions on public attitudes towards refugees is limited, especially compared to individual characteristics. In the second empirical chapter, I argue that individuals exposed to dominant nationalistic views will be less accepting of refugees, while those with composite nationalistic views will be more supportive of refugees. Furthermore, those exposed to dominant conceptions of nationalism will become less accepting of refugees, while those exposed to composite conceptions of nationalism will become more accepting of refugees. Based on my survey experiment of college students in Northern Texas, I conclude that the influence of individual conceptions is a stronger predictor of attitudes toward refugees than exposure to conceptions. In the third empirical chapter, I argue that individuals living in an integrationist nationalistic substate will be less supportive of refugees compared to individuals living in the rest of the composite nationalistic state. Furthermore, individuals who have more of a composite conception of nationalism will be more supportive of refugees, while Individuals who have more of an integrationist conception of nationalism will be less supportive of refugees. Based on my case study from the Focus Canada Survey (2022), much like the second empirical chapter, I find that the influences of individual conceptions on public attitudes towards refugees are a stronger predictor than exposure to conceptions. Overall, the findings of this dissertation suggest that, at least concerning attitudes towards refugees, conceptions of nation and national community have little impact.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".