Quality and Acceptance of Crowdsourced Translation of Web Content
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
Organizations make extensive use of websites to communicate with people. Often, visitors to their sites speak many different languages and expect that they will be served in their native language. Translation of web content is a major challenge for many organizations because of high costs and frequent changes in the content. Currently, organizations rely on professional translators or machines to translate their content. The challenge is that professional translations is costly and too slow while machine translations do not produce high quality or accurate translations even though they may be faster and less expensive. Crowdsourcing has emerged as a technique with many applications. The purpose of this research is to test whether crowdsourcing can produce equivalent or better quality translations than professional or machine translators. A crowdsourcing study was undertaken and the results indicate that the quality of crowdsourced translations was equivalent to professional translations and far better than machine translations. The research and managerial implications are discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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