Bibliographic record
Abstract
This richly detailed and well-argued study truly enhances our understanding of the DP (displaced person) phenomenon. Each chapter discusses one aspect of the DP experience. While the introduction provides a—some-what outdated—overview of migration research, the second chapter presents the contours of “The Problem.” It accounts for the historical context, and it gives us an idea of who the DPs were. It analyzes the discourse on population growth, national development, international responsibilities, and labor market needs prior to the arrival of the DPs in Australia, the United States, and Canada. Henriette von Holleuffer argues that the organization and implementation of DP reception programs depended on the national trajectory and nation-building project of the receiving nation. The third chapter, “The Decision,” tells how the programs were designed, who lobbied, who was in charge, who cooperated, and what were the motives. In the fourth chapter, entitled “Settlement Process,” the various placement programs are outlined by which the DPs were distributed throughout the country, housed, fed and taken care of, and—most important—provided with their first jobs. The final chapter, “Incorporation,” discusses the conditions upon which DPs could become citizens of their new home country. Australian-ization, Canadianization, and the American melting pot were meaningful concepts in the design of the respective DP integration programs.
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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 | 0.001 | 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 | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".