Volunteer translators as ‘committed individuals’ or ‘providers of free labor’? The discursive construction of ‘volunteer translators’ in a commercial online learning platform translation controversy
Bibliographic record
Abstract
This study explores the ways in which volunteer translation in a commercial context is discursively constructed. It focuses on volunteer translation at Coursera, one of the world’s largest MOOC providers, and its volunteer translator community, launched in 2014 to offer online learning in multiple languages. This move to mobilize volunteer translators by Coursera, a for-profit company, became controversial as different parties voiced distinct opinions regarding a commercial company’s recruitment of volunteer translators. Using the Critical Discourse Analysis (CDA) framework and drawing on the notion of digital labor, this paper argues that volunteer translation is described by Coursera mostly in terms of a mission and a learner-initiated and community-building activity. This contrasts with the view of many social critics who tend to emphasize profit-making strategies, labor exploitation, and the degradation of the translation profession in their discursive construction of volunteer translation. This study shows that Coursera’s foregrounding of a moral rationale and of philanthropic and non-profit discourses blurs the boundary between for-profit and non-profit contexts and does the ideological work of naturalizing translation without financial compensation in the context of a commercial company.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.050 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".