Beyond Binaries and Polarization? Rethinking Pluralist Inclusion in Immigrant Nations
Bibliographic record
Abstract
Empirically, the papers in this special issue of Nationalism and Ethnic Politics focus on what lies in between the statuses of insider and outsider, namely the often-overlooked conditionality and temporality of inclusion, the messiness of policies aiming to foster ethnic pluralism, the uneven distribution of attitudes among the members of so-called ethnic groups. They strive to overcome binary shortcuts and the ideological polarization that have recently infested intellectual and political discourse and provide answers to the following questions: Who is conditionally included/excluded in comparison to whom, and for what reasons? What does this conditional inclusion/exclusion entail in terms of pluralist rights and possibilities vs. assimilationist requirements? How does this inclusion/exclusion affect the cultural and economic wellbeing of both the (im)migrants and the receiving society? Drawing on these empirial examples and framing them conceptually, this introductory paper shows that while binaries are a staple in the study of nationalism and ethnic politics, they also give rise to theorizations of ethnocultural pluralism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".