Analyzing the Crowdsourcing Model and Its Impact on Public Perceptions of Translation
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
This paper draws on the results of an online survey of Wikipedia volunteer translators to explore, from a sociological perspective, how participants in crowdsourced translation initiatives perceive translation. This perception is examined from a number of perspectives, including the participants’ profiles, motivations and idiosyncrasies vis-à-vis those of individuals involved in other collaborative social phenomena. Firstly, respondents are grouped on the basis of their training background, their current professional status and their former occupation to compare how translation is perceived by volunteers who do and those who do not work in the translation industry. To further understand the range of respondents ’ perceptions of translation, the crowdsourced translation initiatives they participate in are divided into three types: product-driven (localization/translation of free/open-source software projects), cause-driven (not-for-profit initiatives with an activist focus), and outsourcing-driven (initiatives launched by for-profit companies). A comparison between the results of this survey and two others focusing on the motivations and profiles of free/open-source software developers seeks to identify distinctive features of participatory translation practices. The final part of this article discusses how participants in a crowdsourced translation initiative view translation and how the latter is depicted by the organizations behind such collaborative projects.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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