Bibliographic record
Abstract
Abstract Nationalist movements are a subtype of social movement that make political, economic, and cultural claims based on definitions of a national group or “nation.” Like other movements, they can include individuals, small groups, formal organizations, and sometimes political parties, depending on their breadth of support and the kind of state in which they occur. Contemporary examples are numerous: the Quebecois nationalist movement against English Canadian governance, Scottish nationalist challenges in the UK, Irish nationalism in Northern Ireland, Palestinian nationalism in the West Bank and Gaza, Basque and Catalan nationalist parties in Spain, Tibetan and Uighur nationalism in China, and the numerous nationalist fronts—Estonian, Lithuanian, Armenian, Chechen, to name just a few—that challenged Russian dominance and contributed to the fall of the Soviet Union in the late 1980s and early 1990s. Nationalist movements can mobilize politically, culturally, and/or militarily in both democratic and nondemocratic states to challenge prevailing patterns of sovereignty, political representation, economic opportunities, civil rights, and resource distribution. A defining characteristic of most nationalist movements is a link between a collective national identity and claims of territorial sovereignty (Hechter 2000).
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
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".