Bibliographic record
Abstract
While the world’s refugee population reaches record high numbers, countries offering third-country resettlement are increasingly shifting toward policies of exclusion and austerity. This edited volume envisions a more humane future for refugee resettlement. Combining anthropology with a variety of professional perspectives (education, health care, theology, administration, politics, and social work) ethnography is used to demonstrate the efficacy of programs and interventions that create and nurture social capital in culturally specific and accessible ways. The contributors present case studies of resettlement in the United States, England, Australia, and Canada and contend that social networks have an essential role—are the crux—in the reconfigurations of refugee well-being, belonging, and place-making vis-à-vis the bureaucratic limitations of state and institutional factors. This book includes short contributions from refugees, representatives of resettlement organizations, and government officials, including Jhuma N. Acharya, Bimala Bastola, Khada Bhandari, Kiri Hata, Govin Magar, Madhu Neupane, Natacha Nikokeza, Angela K. Plummer, Lance Rasbridge, Chris Sunderlin, David Thatcher, and John Tluang.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".