Was ist neu an der neuen Dolmetschart Community Interpreting? State of the Art in deutschsprachigen Ländern
Bibliographic record
Abstract
In my paper I would like to discuss the specifics of community interpreting (CI) as a relatively new art of interpreting. The following aspects should be taken into consideration: the definition of the term on specific scopes of CI and compared to other types of interpretation as well as a brief overview of the development of this type of interpreting in Sweden, USA and Canada as pioneer countries and German-speaking countries. For the definition of CI compared especially to conference interpreting, the following factors are important: the particular communication situation, the additional skills of the interpreter, guidelines for CI as well as questions of quality management and the role of the interpreter: Is the tendency to over-emphasize the position of power of the community interpreter fair? In the context of a country-specific overview, the following are also discussed: the professional status of the community interpreter and the situation on the labor market – in areas of court interpreting (including asylum procedures, eg Legal Law Clinic in Innsbruck, Austria as a positive example) and hospital interpreting – and training for community interpreters at university (eg in Graz and Vienna in Austria). In conclusion, I would like to speak about necessary steps to professionalize CI in German-speaking countries.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".