Recommendations for Comprehensive Immigration Reform in the United States
Bibliographic record
Abstract
In January 2019, fifteen students began meeting in an undergraduate seminar on Collective Intelligence. The goal of the course was to leverage group thinking to address a “big” issue of the contemporary world – comprehensive immigration reform. The first half of the course was dedicated to understanding the theories and applications of Collective Intelligence. The second half was applying those theories to the very real issue of immigration reform in the United States. To gain a theoretical foundation, students conversed with international scholars and activists in the collective intelligence field such as Philosopher Pierry Lévy the University of Ottawa, Geoff Mulgan - Chief Executive of the National Endowment for Science Technology and the Arts and Visiting Professor at University College London, the London School of Economics, and the University of Melbourne, Mathematician Nikos Salingaros of the University of Texas at San Antonio, Daren Brabham, Senior Director Analyst at Gartner, and Anita Williams Woolley, Associate Professor of Organizational Behavior and Theory at Carnegie-Mellon University. During the second half of the semester, class members, with the assistance of students at Sorbonne Université in Paris, conducted original research on comprehensive immigration reform. They met with representatives of several immigration, refugee, and asylum organizations including the Center for Refugee Services, Catholic Charities, and the City of San Antonio’s Immigration Office. They conducted face-to-face interviews with approximately 50 students, faculty, and staff at the university seeking input on creative solutions. Significantly, they also implemented two online surveys – one targeting individuals currently living in the United States, and one targeting those living in other countries. The goal of the former was to better understand the current perceptions of the U.S. immigration system and provide suggestions for change specifically related to that system. The latter was solely interested in finding original solutions to the many obstacles of immigration reform, specifically targeting the areas of 1) entry, 2) visas, 3) legal processes, and 4) services. In all, the two U.S.-based surveys (one distributed in English and one in Spanish) yielded a combined 478 responses and the international survey asking for creative solutions yielded 50 responses from 17 countries. Complete results from this survey are included in Appendix A of the white paper.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.031 | 0.018 |
| Insufficient payload (model declined to judge) | 0.055 | 0.008 |
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".