Quality and Efficiency: Incompatible Elements in Translation Practice?
Bibliographic record
Abstract
The aim of this article is to describe the quality assessment procedures in a large, national translation company. The company is more than ten years old, but the past five years' growth rates have been rapidly increasing. The growth in turnover can be attributed both to a high degree of customer loyalty based on a high level of efficiency and trust, and on high, well-defined and transparent quality standards. The company is based on the idea that translators should function in a working environment based on full-term employment. Consequently the increase in turnover has involved recruiting a large number of translators and support services in the IT-department. This is why quality assessment procedures are no longer an individual responsibility, but have become a corporate issue. Quality procedures must therefore be part of the daily routines and involve all aspects of the business. To understand the conditions of the translation market today, the author provides an overview of the market based on the ASSIM-study and information on the new economy. After that she presents the case of Translation House of Scandinavia and finally she discusses some of the possible quality assurance systems that are available today and are used by the translation industry.
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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 | 0.058 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.037 | 0.040 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".