Bridges and Barriers in Public Service Interpreting Training: Instructing Non-Professional Longserving Interpreters
Bibliographic record
Abstract
This paper reports on an action-research study with a dual purpose: (1) to design a PSI training programme adapted to the needs of the City of Québec’s public healthcare institutions, and (2) to assess its contribution to the development of trainees’ PSI competences. The course was designed adapting ÉSIT’s special regime methodology to PSI training, and delivered to a group of non-professional interpreters (N=23). The evaluation was undertaken qualitatively, through two focus groups (n=11). The data collected was submitted to content analysis and contrasted with the trainer’s action-research report. Findings reveal (1) that the special regime methodology can be applied to PSI training programmes, if combined with pedagogical approaches adjusting it to the group’s needs, and (2) that trainees’ preconceptions about PSI add up to the list of challenges of training non-professional longserving interpreters. Our concluding remarks present several recommendations on how to overcome the detected difficulties.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".