A Holistic-Componential Model for Assessing Translation Student Performance and Competency
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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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".