A preliminary theoretical investigation into [online] social self-translation: The real, the illusory, and the hyperreal
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 article argues that “social translation”, “crowdsourced translation”, and “user-generated translation” are in fact not synonymous. Building on previous research, the term “social translation” is used to refer to translation activity that takes place on various online social media and that engenders specific online social affordances. While crowdsourced translation in online settings continues to garner interest from translation scholars, very little has been said on the subject of self-translation in online and digital contexts, specifically with regard to social media. This article begins filling this gap by first defining [online] social self-translation and providing a taxonomy of different types of self-translation under this umbrella term. Examples are offered to illustrate the categories “real”, “illusory”, and “hyperreal”. Theoretically examining social self-translation sheds light on how self-translation phenomena occur online and how such activity can help translation studies scholars rethink the “self”, the “social” and, thus, self-translation and social translation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.002 | 0.003 |
| 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