“Creative Shifts” as a Means of Measuring and Promoting Translational Creativity
Bibliographic record
Abstract
Thanks to Paul Kußmaul, the investigation of translational creativity has made considerable progress. The measurement of creativity, however, has remained a great challenge. The following article presents the results of the measurement of one aspect considered central to the notion of translational creativity, namely the measurement of the ability to depart from the source text (ST) structure by applying creative shifts , i.e., abstracting, modifying or concretising source text ideas in the target text (TT). Sixteen units of analysis from 4 experimental texts translated by 11 students of translation and 5 professional translators each were analysed with the aim of finding out how many of them constituted creative shifts as opposed to mere reproductions of the source text. The results of this sample analysis reveal that there are clear differences between student and professional behaviour and that a certain trend for the development of creative competence can be established. Moreover, these results do not only point to a methodologically interesting approach for analysing complex cognitive constructs, but they also provide a valuable starting point for pedagogic research and application.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".