The Influence of Adult Education Principles on Canadian Spoken-Language Interpreter Training Programs: A Case Study
Bibliographic record
Abstract
This thesis reports on a one-year, multi-site case study aimed at characterizing current approaches to interpreter training in Canada and determining the influence of adult education principles on that training. The theoretical framework utilized was Malcolm Knowles’ (Knowles, Holton III, & Swanson, 2015) six assumptions about adult learners and eight andragogical design elements. Data collection was carried out in two stages: a recruitment survey sent to all Canadian interpreter training programs known to the research, followed by an in-depth visit of four programs involving document review, 44 hours of observation and 29 interviews. Findings indicate that Canadian programs are primarily utilizing a master-apprenticeship approach to training, which seems to be working well. The programs observed also appear to be incorporating many adult education principles. Additional contributions from the field of adult education are discussed; it is argued that sharing research and expertise in areas such as confidence and motivation building, content and feedback delivery, facilitation techniques, characteristics of adult learners, the strengths and weaknesses of the apprenticeship model, and the role of learning styles, would be beneficial to interpreter trainers and trainees alike.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".