Bibliographic record
Abstract
Narrating is a human instinct—by narrations, the past exposes itself to us, enabling a communication that would not have been possible in the temporal and geographical distanciation, as well as generating an “I” that understand the others as a part of oneself and oneself as a extension of others. From this perspective, translation is, to some extents, narrating, but of more cultural significance. This essay serves as an inquiry into the border between narrative and translation, expounding the primary form “mimesis” by which human experience is made meaningful and which gives the shape and meanings to human life. Mimesis crystallizes the link between translation and historical truth, linguistic hospitality and cultural co-existence and this essay explores the link from the vantage points of Paul Ricoeur’s narrative theorizing on the importance of narrative as the expression of experience, mode of communication, and path to understanding the world and ultimately ourselves. Presenting a variety of perspectives from narratology and translation studies, the essay hopes to discourse the intricacies narratives and translation process, highlights how translation imitates the original writings, events and forms of lives and represent them into new narratives.
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.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".