Bibliographic record
Abstract
Because crowdsourced translation initiatives rely on volunteer labour to support both for-profit and not-for-profit activities, they lead to questions about how participants are remunerated, how the perception of translation is affected, and how minority languages are impacted. Using examples of crowdsourced translation initiatives at non-profit and for-profit organizations, this paper explores various ethical questions that apply to translation performed by people who are not necessarily trained as translators or financially remunerated for their work. It argues that the ethics of a crowd-sourced translation initiative depend not just on whether the initiative is part of a not-for profit or a for-profit effort, but also on how the project is organized and described to the public. While some initiatives do enhance the visibility of translation, showcase its value to society, and help minor languages become more visible online, others devalue the work involved in the translation process, which in turn lowers the occupational status of professional translators.
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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 | 0.142 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.086 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".