Bibliographic record
Abstract
Abstract This article interrogates the position of refugees and asylum-seekers in the global political economy of migration. With the advent of increased displacement since the mid-2010s, refugees have faced simultaneous policies of exclusion and/or extraction. What was originally framed as a crisis of forced displacement based on the war in Syria has now become a feature of contemporary capitalism where legacies of colonialism, climate change, and economic insecurity dovetail. Hotspots of displacement have grown in Africa, Asia, and Latin America and many states in the Global North and Global South have now come to the realization that integrating displaced people into their states, cities, and communities is a long-term consideration requiring cooperation across multiple levels of government. A key aspect of contemporary refugee governance is the concept of value, and this concept guides the central question of this article: who benefits and why from refugee inclusion or exclusion? Drawing on fieldwork in Europe and East Africa in conjunction with policy analysis, this article explores two interrelated dimensions of contemporary refugee governance: (i) border externalization and (ii) entrepreneurialism. As such, this article explores various policy frameworks such as the offshoring and entrepreneurship as potentially value-generating activities that guide the governance of refugees and migrants in contemporary capitalism. Citation: Bhagat, Ali, ‘Refugee Governance Under Racial Capitalism: Exclusion and Extraction in the Age of Ongoing “Crises”’ (20 Mar. 2025), in Beverley Mullings (ed.), Labour and the Economy, in Meena Dhanda (ed.), Oxford Intersections: Racism by Context (Oxford, online edn., Oxford Academic, 20 Mar. 2025 -), https://doi.org/10.1093/9780198945246.003.0079, accessed [date].
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".