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Record W2999554031 · doi:10.11575/prism/37330

The Influence of Adult Education Principles on Canadian Spoken-Language Interpreter Training Programs: A Case Study

2019· dissertation· en· W2999554031 on OpenAlexaboutno aff
Jeffrey Glen Patrick Staflund

Bibliographic record

VenueOpen MIND · 2019
Typedissertation
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsInterpreterLinguisticsTraining (meteorology)Spoken languageComputer scienceMathematics educationPsychologyPedagogyNatural language processingProgramming languageGeography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0210.005
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.103
GPT teacher head0.494
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicInterpreting and Communication in HealthcareFrench-language works237,207