The Lesser Violence Than Murder and the Face-to-Face: ‘Illegal’ Immigrants Stand Over American Law
Bibliographic record
Abstract
Neticia, a 23-year-old mother of two boys, aged 3 and 5, has been left behind by her group crossing the US border from Mexico. She is stranded in the harsh Arizona desert. On the 14th of July, the ‘cruel temperatures of that cloudless sky reach 116 degrees, an intensity of heat that forces all life into survival mode,’ according to the young humanitarians who volunteer to bring food, water, and medical care to desert migrants under the banner of the nonprofit group No More Deaths find Neticia. They remark on [t]he cruel current of maternal desperation and the promise of the American dollar [that] pulled her from her home in Oaxaca and from her two young boys … She could not bear the thought of turning back empty-handed, because her youngest child needs expensive medical treatment. Yet she fell during the night and hurt her knee, and then found herself alone, immobile, lost, and without food or water. To abandon her would surely have meant inevitable death. Neticia is a face among faces. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".