Machine Translation and Translation Memory Systems: An Ethnographic Study of Translators’ Satisfaction
Bibliographic record
Abstract
The translator’s workplace (TW) has undergone radical changes since microcomputers were introduced on the market and, as a result, digitization increased enormously. Existing translation-related technologies, such as machine translation (MT), were enhanced and others, such as translation memory (TM) systems, were developed. It is a noteworthy fact that implementing new translation-related technologies in the TW is done in various conditions according to specific goals that subsequently define new work conditions for translators. These new work conditions affect translators’ satisfaction with their job, and their satisfaction will influence career development and employee retention in the translation industry over the long term. In the past two decades, Language Service Providers (LSPs) have started integrating MT into TM systems to benefit from MT suggestions when TM is not helpful. Neither TM nor MT is unfamiliar to the translation industry, but the combination, i.e. TM+MT, is fairly new. So far, there have been few studies on translators’ satisfaction with TM+MT. This study consists of an ethnographic research project on seven translators in a Canada-based company where TM+MT is used. Observations, semi-structured interviews, and in-house document analysis have been used as data collection methods. The data obtained has been analyzed and discussed based on Rodríguez-Castro’s task satisfaction model (2011). This model addresses intrinsic and extrinsic sources of translators’ satisfaction with the activities they do in their job. Investigating the factors and variables of her model in the aforementioned company, I concluded that those sources of satisfaction cannot be considered separately from the job-context factors, such as the company’s policies in implementing TM+MT.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".