Bibliographic record
Abstract
Community interpreting and translation enable public service providers and users to communicate in situations where they do not share the same language. These professions are essential for social equity and egalitarian access to legal, health, education and other services. Many countries with significant numbers of migrants or autochthonous language minorities have developed more or less satisfactory services and standards in this burgeoning subfield of translation and interpreting. Instances can be identified of countries that have made significant progress (e.g., Australia, Canada and Sweden) as well as of those which started only recently (e.g., Spain and Italy). In Arab countries, however, one can hardly find a reference to this subfield of translation studies, although situations requiring such interpreting and translation services are numerous. This paper describes and raises awareness of the status quo of community interpreting and translation in the Arab World. Three examples are focused on: Morocco as a country with a national language minority, the United Arab Emirates, as an affluent country hosting migrants, and Saudi Arabia, a country with a special religious position which hosts millions of pilgrims every year. The paper also includes recommendations based on migration and pilgrimage statistics and the experiences of the pioneering countries above.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".