Community Interpreting and linguistics: A fruitful alliance? A survey of linguistics-based research in CI
Bibliographic record
Abstract
Since the first Critical Link Conference in Geneva Park, Canada, in 1995, Community Interpreting (CI) has experienced a dramatic change in both theory and practice. National and international conferences, seminars, courses, and workshops all around the world have made it possible for practitioners, trainers, and researchers to get together and discuss their views and exchange ideas. At the same time, an ever-growingflow ofpublications reflects the enormous activity in this field. Nevertheless, CI re- search is still farfrom being in the same category as infields such as conference interpreting or translation, and this is all the more so for linguistics-based CI research.As a researcher working in a department mostly involved with linguistics and related areas but with an increasing interest in cultural studies and translation studies, it is my intention to analyze and classify the contributions to CI conferences and the publications of CI papers using a linguistics-based methodology. To begin with, the evolution of linguistics and those sub-areas, which have had the greatest influence in the lastfew decades, will be briefly discussed, as will its methodologies. Secondly, an analysis will be presented of the characteristics and tools of linguistics-based CI research. And thirdly, conclusions will be drawn concerning the evolution, trends or gaps in CI research in general, and in linguistics-based CI research in particular.
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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 | 0.034 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.022 | 0.043 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".