A Holistic-Componential Model for Assessing Translation Student Performance and Competency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Translation quality assessment (TQA) tools frequently come under attack because of the myriad variables involved in TQA: the definition, number and seriousness of errors, the purpose of the assessment, evaluator competence and reliability, the client's or end user's requirements, deadlines, complexity of the TQA model, etc. In recent years, progress in factoring in these variables and achieving greater reliability and validity has been achieved through functionalist, criterion-referenced models proposed by Colina (2008, 2009) and others for the assessment of professional translation quality, even though they have come under attack from proponents of the normative assessment model (Anckaert et al., 2008, 2009). At the same time, progress has been made in student assessment through the holistic, criterion-referenced approaches developed by education theorists Wiggins (1998) and Biggs and Tang (2007) ─ approaches that have been applied to translation by Kelly (2005). In this article, the author proposes a "holistic-componential" model for translation student assessment. Based on a combination of Colina's functionalist translation assessment model and the holistic student assessment model and drawing on definitions of professional standards applied in North America, it is designed to rectify some of the perceived shortcomings of the conventional quantitative, error-based marking schemes, those of the more "impressionistic" schemes, and even those of criterion-referenced models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it