Social and economic actors in the evaluation of translation technologies. Creating meaning and value when designing, developing and using translation technologies
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
Evaluation of translation technologies is a social activity, which involves the establishment of knowledge communities as well as the creation of competition to produce better tools. Companies developing translation technologies need to encourage the evaluation of their tools (through online forums, discussion lists, blogs, product communities, community translation, etc.), since evaluating the technology implies spreading and sharing knowledge about it; and sharing the same knowledge or the same modes of thinking and operation, rather than sharing the same material resources, represents the basis of future economic competition. When exchanging knowledge about technologies, translators engage in social activity: they express their opinions and feelings about the technologies they are using, they make judgments about the worth or value of a specific technology, they influence others’ decisions or they believe their thoughts will have an impact on decisions companies will make. This article investigates the use of translation technology evaluation criteria as they are represented in several translators’ communities and it calls for a multidisciplinary approach when analysing translation technologies adoption, use and evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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