A Dream Deferred: Ending DACA Threatens Children, Families, and Communities
Bibliographic record
Abstract
* Abbreviation: DACA — : Deferred Action for Childhood Arrivals > What happens to a dream deferred? Does it dry up like a raisin in the sun?. . . Or does it explode? > > Langston Hughes On June 15, 2012, the Obama administration issued a memorandum providing protection from deportation for a group of immigrant adolescents and young adults who were brought as children to the United States without authorization. This memorandum, Deferred Action for Childhood Arrivals (DACA), has to date protected nearly 800 000 of the 1.9 million potentially eligible individuals (Table 1), including 228 000 children <15 years old who would age into eligibility.1 Over half of DACA recipients are <21 years old, one-quarter are parents of US-citizen children, and 70% have family members who are US citizens.2 Although DACA does not provide a permanent lawful immigration status and is only a piece of policy needed to support immigrant families, DACA allows youth to receive Social Security numbers, obtain driver’s licenses, seek higher education, and become legally authorized to work. DACA permitted those who consider America their home to finally feel at home. However, on September 5, 2017, the Trump administration announced it would end the program. As a result of this decision, nearly 800 000 current DACA … Address correspondence to Omolara T. Uwemedimo, MD, MPH, Department of Pediatrics, Cohen Children’s Medical Center, 269-01 76th Ave, New Hyde Park, NY 11040. E-mail: ouwemedimo{at}northwell.edu
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".