Are Citizen Science “Socials” Multilingual? Lessons in (Non)translation from Zooniverse
Bibliographic record
Abstract
Abstract Few studies outside Translation Studies examine the presence and role of translation in the social media landscape, particularly beyond the “big” players (e.g., Facebook; Twitter)—as if the exchange of content occurs seamlessly in these inherently multilingual and multicultural contexts. This study examines a distinct social platform (“Zooniverse”) that links academe and citizen scientists. The chapter examines how and to what effect translation is mobilized to produce and disseminate (scientific) knowledge on social platforms/social media. This research builds on previous work investigating the motivations of volunteer translators in citizen science, but it is distinct in its methodology: instead of examining volunteer motivations, the focus is on the presence (or lack thereof), as well as the role(s) and effect(s) of translation in relation to linguistic representation, knowledge production, and knowledge dissemination.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".