Bibliographic record
Abstract
We are delighted to present the January 2025 issue of the Journal of Languages and Translation, the first issue of Volume 12. This issue brings together a wealth of expertise, with articles covering various topics in linguistics, literature, and translation studies. The contributions reflect the dedication and effort of everyone involved in producing this issue.We would like to express our gratitude to the editorial board for their guidance and to the reviewers for their valuable feedback, which has been crucial in upholding the high academic standards of the journal. Also, we sincerely thank our language editors for their meticulous efforts in ensuring the quality and clarity of each article.This issue was prepared during a busy period, as many contributors and team members were managing coursework, teaching, and other professional responsibilities. Despite these challenges, they showed great commitment and perseverance, overcoming all obstacles to complete this issue successfully.We hope you find the articles in this issue engaging and thought-provoking. We believe the insights shared here will enrich discussions in the fields of languages and translation and inspire further research.Sincerely,Marwa Muhammad Wagdy El ShereieEditor-in-Chief &Dean, Faculty of Al-Alsun, Minia University Minia University, EgyptEmail address: famu.journal@mu.edu.eg
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.888 | 0.784 |
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".