Bibliographic record
Abstract
Basically, the history of MT research can be divided into 6 phases. The stages used in \nthis thesis are based on Hutchins’ essays written about MT history. The first phase lasts till \n1956 with the first ideas of computerised translation. Before this ideas of using machines for \ntranslation emerged but the actual research started in the first half of the 20th century as \ntechnical circumstances became adequate only in that period. However, a few facts are \nmentioned in this paper about this area before the computer. The second stage is characterised \nwith high enthusiasm but the ALPAC report in 1966 had a great impact on MT development \nand founding. However, it had negative consequencies, research did not stop completely in \nthe so-called ’quiet decade’ afterwards when the centre of development shifted from the \nUnited States to Canada and to the European Community. After this decade a revival of \nresearch can be observed, systems for commercial purposes appeared and not only \nuniversities and academies took part in development processes but also technological \ncompanies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.020 |
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".