Social and economic actors in the evaluation of translation technologies. Creating meaning and value when designing, developing and using translation technologies
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.
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 | 0.112 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".