Bibliographic record
Abstract
This article proposes that activist translators be involved and engaged in those legal realms, such as the treatment of “illegals” or undocumented migrants, because this is an area in which translators can act as true intermediaries, over and above the act of substituting one lexical item for another; however, this form of activism, like other discretionary activities, needs to be directed to lofty causes, such as upholding the human rights of those most excluded by our society. In other words, alongside of the activism must come good faith, because “activism” could also actively hurt the person for whom the translator is doing his or her task. In other words, when the “translator” decides to become an “interpreter,” there is the danger that the subjectivity of the latter will trump the “objectivity” of the former, with negative consequences. This article advocates activism over machine-like fidelity because the abuses in certain realms of law are so egregious and the stories so horrendous that most translators who are given the right to speak out will take the road towards humanity and basic decency. The examples to which I will be referring emanate from the realm of immigrant incarceration in the Southern US, so for the purposes of this article positive activism points to efforts that help people who are arrested in the United States (or anywhere else) for violations of immigration laws. Regrettably, the kind of activism for which this article advocates is not likely to occur, not only because translators are not “supposed to be” activists, but also because the realm of law that deals with immigration violation is so unevenly applied, so internally inconsistent across local, regional, state, federal and national lines, and so variously construed depending upon the person doing the construing, that it does not really deserve the nomenclature of “law.” Keywords: translation, interpretation, incarceration, administrative law, undocumented migrants.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".