Translators as publishers: exploring the motivations for non-profit literary translation in a digital initiative*
Bibliographic record
Abstract
Motivations for volunteering have rarely been studied in the context of professional literary translators. Instead, studies have mainly focused on amateur translators in areas such as charities, fansubbing, TED, Wikipedia, Skype and Facebook. This paper explores this under-researched topic in the context of ¡Hjckrrh!, a non-profit publisher led by translators who self-publish literary translations in e-book format. As of March 2018, ¡Hjckrrh! had issued 21 e-books translated from seven languages, with the collaboration of fourteen translators. Most of the translators are experienced professional translators with full-time jobs. Based on in-depth semi-structured interviews with fifteen participants, this paper explores the translators’ motivations for collaborating on this initiative and shows how an ethnography-inspired methodology can be fruitful when studying translators. The outcomes reflect that translating for pleasure and personal relationships are factors that trigger translators’ voluntary participation in ¡Hjckrrh!, and the conclusions highlight the need for more research into similar non-profit publishing initiatives.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".