Emerging Themes and Issues in Ethnicity, Nationalism, and Migration Research
Bibliographic record
Abstract
Abstract Ethnicity and nationalism, interethnic conflicts, and human migration have been major forces shaping the modern world and the structure and stability of contemporary states. A notable reason for the current academic interest in ethnicity and nationalism is the fact that such phenomena have become so visible in many societies that it has become impossible to ignore them. In the early twentieth century, many social theorists claimed that ethnicity and nationalism would decrease in importance and eventually vanish as a result of modernization, industrialization, and individualism, but this never came about. Instead, ethnicity and nationalism have grown in political importance in the world, particularly since the Second World War. It is important to note that ethnicity and nationalism are social and political constructions, as well as modern phenomena that are inseparably connected with the activities of the modern centralizing state. One characteristic of a modern state is the presence of population diversity brought about by migration. Human migration can be defined as the movement by people from one place to another with the intentions of settling permanently in the new location. One of the reasons why immigrants choose to migrate to another country is because globalization has increased the demand for workers from other countries in order to sustain national economies. Known as “economic migrants,” these individuals are generally from impoverished developing countries—usually people of color—migrating to obtain sufficient income for survival.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| 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".