Bibliographic record
Abstract
How can we think, imagine, and make authoritative claims about contemporary refugee politics? I believe this question must precede investigations into struggles/movements advocating rights and political voice for refugees. It is important to come to terms with the changing terrain of refugee politics, in order to (re)conceptualize it and provide some idea of how/where such struggles might be fought. Focusing on the colliding commitments to globalization and security, particularly since September 11, 2001, I argue that “paradox” is a core element of refugee politics. To some extent, this has been rehearsed elsewhere, and I point to the highlights in the existing literature. I suggest that an approach sensitive to Foucault’s account of governmentality and biopolitics is particularly helpful, stressing the diffuse networks of power in refugee politics among private and public actors, the increasing role of “biotechnology,” and some (re)solution to the globalization – domestic security paradox, leading to what I call the “biopoliticization of refugee politics.” Examined here are the politics of asylum and refugee movements in the UK. In particular, the 2002 government White Paper on immigration and asylum – Secure Borders, Safe Haven – provides an example of the changing terrain of contemporary (post-September 11) refugee (bio)politics.
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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.097 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".