Public Service Interpreting and Translation: Moving Towards a (Virtual) Community of Practice
Bibliographic record
Abstract
Following many battles, Public Service Interpreting and Translation (PSIT) is gradually evolving towards professionalisation. Wherever it is practiced, common issues have been identified: defining the profession, providing interpreting services for rare or minority languages, educating stakeholders, moving from training to education, and last but not least interpreting and translation quality. The lack of funding for PSIT courses within the current financial context is forcing stakeholders to work differently. The community of practice model can help PSIT stakeholders share resources and knowledge beyond the traditional boundaries set by courses, schools or countries. New technologies such as virtual conference tools and shared repositories are the essential “missing link” towards the progress of PSIT education. PSIT stakeholders need to join forces and pool efforts towards a constructive and innovative dialogue that would enhance the profession. Some forms of PSIT, such as legal interpreting and translation, have already broken barriers and gained professional recognition. However, PSIT should include all forms of PSIT contexts, including the medical or local government. Finally, once fully defined, PSIT and conference interpreting for spoken and sign languages could finally come together under the Interpreting profession umbrella. This is the achievable ultimate aim when working as communities of practice, small or large, language specific or generic, face to face or virtual.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.059 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".