Bibliographic record
Abstract
The gradual introduction of Translation Memory in translation workplaces, starting in the late 1990s, has created a classic industrial conflict. Managers and clients of translation services want to increase productivity, but translators do not want to be told how to produce translations, and they do not want to see their incomes reduced. While the technical features of Memory programs certainly cause dissatisfaction, all technologies have defects, and a key question then is who decides how to deal with these defects—translators? or managers and clients? As a result, policies on the use of Memory in a workplace become crucial. There are objective policy questions: Are translators’ productivity requirements increased when Memory is introduced? Do translators receive less pay for matches found in the Memory database? Is the translator allowed to search the Memory database? Then there is the subjective aspect: how do translators feel about their own experience of whatever is objectively happening? Do they feel they are in control of the texts they are producing? Are technologies increasing or decreasing their satisfaction with their working lives? Do they have a sense of losing the ability to compose their own translations or are they equally happy to revise wordings proposed by Memory? Do they feel that use of these technologies is reducing or enhancing the status of translators in society? This article looks at some of these subjective matters, based on two surveys of Ontario translators conducted in 2011 and 2017.
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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 | 0.031 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".