Bibliographic record
Abstract
Social, cultural, and legal practices associated with international migration are integral elements of a wider neoliberal regime of accumulation. Neoliberalism, however, is not a monolithic configuration. It evolved through a history and geography of experimentation (Peck 2004) and exists in a variety of forms. Likewise, the manner in which international migration regulates labor markets does not follow a prewritten, universal script but evolves in a place- and contextspecific manner. Formal citizenship, for example, is a powerful category to control migrant labor in many countries. In Canada, however, foreign immigrants and citizens have similar labor market rights, and in Germany long-term foreign residents acquire postnational rights, which put newcomers on more or less equal legal footing with nonmigrants. When citizenship fails to distinguish between migrant and nonmigrant workers, then other mechanisms of distinction, including various forms of cultural and social capital, assume more prominent roles. The case studies presented in this book show how these legal, social, and cultural processes of distinguishing and controlling international migrants regulate labor markets. Cultural representation is a critical process in maintaining, enforcing, and advancing this aspect of the neoliberal project. A particularly powerful discursive strategy is the representation of migrant labor as essential for production and economic well-being and, at the same time, the vilification of migrant workers as outsiders, parasites, and threats to local and national communities. Although I limited my empirical investigation to a few case studies, similar representations of migrant workers likely exist in Australia, throughout Europe, in the United States, and in other migrant-receiving industrialized countries. In recent years, cultural representations of migrants have been tied to the so-called war on terrorism, which constructs international migrants as a particularly deadly population. Exploiting the fears of terror, restrictive and oppressive policies and practices toward international migrants have gone far beyond genuine efforts to filter out traveling suicide assassins (Wright 2003). The strategic incorporation of new narratives into discourses of migration and the appropriation of relatively unrelated but highly visible events such as the destruction of the World Trade Center in New York illustrate the systematic, if not deliberate, nature of representation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".