The shape of translation policy: a comparison of policy determinants in Bangor and Brownsville
Bibliographic record
Abstract
The idea that there is something scholars can call “translation policy” has existed since the very early days of the field. As studies continue to spring forward, new perspectives continue to help our understanding of how translation policy evolves. Generally speaking, these studies tend to look closely at the role that the authorities play in shaping translation policy. Such an approach has led to useful insights, and for the foreseeable future, it will probably continue to provide enriching perspectives. But oftentimes, translation policy takes shape in official domains as a response to factors outside the domains themselves. In other words, there are insights to be gleaned by looking beyond the official domains. For example, interesting perspectives may come from looking at broader historical and demographic determinants that help shape translation policy. In that spirit, this paper will consider translation policy in two different settings: Gwynedd (Wales) and Cameron (Texas). It will compare and contrast these two regions in terms of history and demography, where some surprising parallels can be found. Then the article will describe translation policies in both places, where some stark contrasts become immediately apparent. Then this paper will analyze these differences in terms of how the minority language is viewed in these regions.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".