A Snapshot of Immigration Court at Stewart Detention Center
Bibliographic record
Abstract
Changes to U.S. immigration policy have implications for social work, owing to the ethics-based foundation of the profession. The twin purposes of this mixed-methods case study are to describe the detainment and deportation processes, and their implementation at the Stewart Detention Center in Lumpkin, Georgia (notorious for its disparately high rate of deportation); and, to discuss social work’s ethical role in relation to immigrant populations and immigration policy issues. To achieve analysis, we collected data at the Stewart Detention Center, directly observing a sample of 39 immigration court hearings across 4 separate time points between June and September of 2018. We use univariate statistics to describe the sample in terms of hearing duration, demographics of detained persons, characteristics of judge and attorney interactions, removability charges, and hearing outcomes. Our analysis includes mappings of 2 courtrooms and a thick and rich narrative description of our first trip to the Stewart Detention Center in June, 2018. Identified themes across court observations include: (a) lack of uniformity in process, (b) adoption of criminal justice norms and procedures without inclusion of protective factors, (c) layers and barriers to communication that present as isolating, and (d) a seeming emphasis placed on the voluntary departure option. We conclude with a discussion of implications for social work and respective recommendations for engagement.
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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 | 0.002 |
| Science and technology studies | 0.018 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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".