A New Deal for Refugees: How Land Restoration and Infrastructure Modernization Can Help Solve the Refugee Crisis
Bibliographic record
Abstract
The migrant crisis in Europe is the largest since WWII. Millions of migrants are without hope or a source of income. However, this tragedy could present an opportunity for Europe as the EU currently faces two problems, which a large influx of labor could fix. First, decaying outdated European infrastructure and construction and secondly the abandonment of European farmland. During the 1920’s the Civilian Conservation Corps (CCC), a government program from the great depression, employed large numbers of unemployed men in land management fields. Just as the CCC addressed the environmental and social problems of its day, a modern government program modeled after the CCC could address the issues currently facing the EU. This paper lays out a roadmap for just such a program outlining how the EU could employ refugees to address the current environmental issues facing Europe. The paper also explores potential uses for abandoned farmland, such as re-utilization for agriculture, agroforestry and re-wilding as well as the management, funding and potential issues faced by such a program.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".