E-lectra: A Bibliography for the Study and Practice of Legal, Court and Official Translation and Interpreting
Bibliographic record
Abstract
Scientific development presupposes the efficient communication of new findings. The increase in the number of academic journals and publications worldwide burdens scholars, also translation scholars, with the responsibility of keeping up to date with ever increasingly scattered relevant literature. On the other hand, legal translation professionals need to select and find specialised documentation and reference material, which the market addresses not to translators or interpreters but to law specialists. E-lectra is an electronic bibliographic database whose purpose is to help legal translation scholars and practitioners select and find relevant literature and documentation resources, and further help researchers to present their work by providing a powerful pool of formats and styles incorporated in an easy-to-use system for citing references in their works and adapting them to the conventions of different journals from the field of translation studies.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.033 | 0.047 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.041 |
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".