Making WikiMedia resources more useful for translators
Bibliographic record
Abstract
In this paper, we explore the concept of a wiki translation resource, that is, a free, open, massively collaborative wiki based resource that translators could use to find translations of problematic words, terms or expressions. Using field data collected through contextual observation and interviews of translators in their workplace, we specifically investigate three research questions. First, what might be the advantages of a wiki resource, compared to more conventional closed resources currently used by translators? Second, to what extent do existing wiki resources like Wikipedia, Wiktionary and OmegaWiki already constitute a satisfactory translation resource? Third, if existing wiki resources are not useful to translators in their current state, how might they be improved and transformed into a satisfactory translation resource? Regarding the first question, we show how a wiki resource might improve on the myriad of online resources currently used by translators (bilingual dictionaries, generic or domain specific terminology databases) by providing a single free tool with a wider coverage of all types of translation problems and topic domains. Also, it could achieve economies of scale for freelance translators, by allowing them to share expertise and data within a worldwide community of practice. Regarding the second question, we show that in their current state, existing wiki resources are not very useful to translators because they lack sufficient coverage of typical translation problems. Also, their user interfaces do not make it easy to carry out key translation related tasks such as: finding an appropriate translation for a problem, adding a new translation for a problem, and assessing the trustworthiness of a particular translation for a problem. Regarding the third question, we describe what research and development would have to be done to turn each of those existing wiki resources into a satisfactory translation resource. Based on this, we conclude that OmegaWiki is the most promising platform, and that it can indeed be evolved into a resource that translators could use in their daily work.
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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 | 0.019 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.034 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".