On Not Taming the Wild Tongue: Challenges and Approaches to Institutional Translation in a University Serving a Historically Minoritized Population
Bibliographic record
Abstract
A consequence of the development of modern states has been the concept of “minority” as used to refer to subsets of the population that are differentiated from that portion of the population which is seen as the “majority.” These minorities are at times distinguished from each other using terms such as national minorities and immigrant minorities. Some scholars have challenged the distinctions drawn by these constructs. An example of how such constructs are not always accurate can be found in Texas’ Rio Grande Valley, where ethnic and linguistic, immigrant and national, minority and majority are not always clear cut. “The Valley,” as the region is locally known, has a long history of the numerical majority being in a minoritized position. In this context, a local university administered a “speech test” to Mexican American students who enrolled between the 1950s and the 1970s. The purpose, according to Anzaldúa (1987), was to tame their “wild tongue.” This same university, now transformed, proposes to rehabilitate itself, as it becomes bilingual, bicultural, and biliterate. Accordingly, it now undertakes a systematic effort to bilingualize its operations, starting with the localization into Spanish of its website as conducted by the University of Texas Rio Grande Valley’s Translation and Interpreting Office. A number of terminological strategies and translation challenges stemming from the variegated lectal and diglossic landscapes of the region have arisen, which can be illuminated by the Post-Colonial paradigm found in Translation Studies.
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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 | 0.067 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.049 | 0.104 |
| Scholarly communication | 0.055 | 0.033 |
| Open science | 0.008 | 0.028 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".