Analyzing the Crowdsourcing Model and Its Impact on Public Perceptions of Translation
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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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 | 0.044 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".