A Crisis Transformed: Refugees, Activists and Government Officials in the United States and Canada during the Central American Refugee Crisis
Bibliographic record
Abstract
abstract: During the 1980s hundreds of thousands of Central American refugees streamed into the United States and Canada in the Central American Refugee Crisis (CARC). Fleeing homelands torn apart by civil war, millions of Guatemalans, Nicaraguans and Salvadorans fled northward seeking a safer and more secure life. This dissertation takes a "bottom-up" approach to policy history by focusing on the ways that "ground-level" actors transformed and were transformed by the CARC in Canada and the United States. At the Mexico-US and US-Canada borders Central American refugees encountered border patrol agents, immigration officials, and religious activists, all of whom had a powerful effect on the CARC and were deeply affected by their participation at the crisis. Using government archives, news media articles, legal filings and oral history this study examines a series of events during the CARC. Highlighting the role of "ground level" actors, this dissertation uses three specific case studies to look at how individuals, small groups, and a border town transformed and were transformed by the Central American Refugee Crisis. It argues that (#1) the CARC deeply affected the lives of those who participated in it, and (#2) the actors' interpretation and negotiation of, as well as resistance to, refugee policy changed the shape and outcomes of the Central American Refugee Crisis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.049 | 0.020 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".