Bibliographic record
Abstract
Refugees are often promoted as assets for host states, as investment in their human capital is understood to stimulate economic growth and boost employment in under-resourced industries. Refuge: How the State Shapes Human Potential inverts this discourse to explore the cost of socioeconomic integration policies for refugees in wealthy Western countries. It proposes ‘state-structured human capital’ as a conceptual frame for understanding how the welfare systems refugees encounter shape their economic mobility and social security, by defining what and whose human capital is recognized and considered worthy of investment. To evidence this proposition, Heba Gowayed documents the experiences of 43 Syrian refugees as they grapple with the social and economic realities of ‘refuge’ in the US, Canada, and Germany. Drawing on ethnographic research and interviews conducted in their first years after arrival (2015–18), Refuge paints a picture of men and women striving to enact and advance their human capital on their own terms. Often, this involves struggling against systems that seek to produce them as self-reliant and productive members of society, whilst simultaneously marginalizing them because of their race, religion, gender, and class.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".