Посібник з англійських фразеологізмів
Bibliographic record
Abstract
Із дозволу Кори Андерсон (Канада), яка є автором посібників “Easy Approach to English Idioms and Idiomatic Expressions” у двох томах, ми переробили її оригінальні книги, щоб зробити варіант, який би годився для учнів середніх шкіл та студентів університетів в Україні. У цій книзі подається український переклад для кожного англійського виразу. Словникові статті є тими ж, що й у посібниках Кори Андерсон, за винятком декількох виразів, які широко не використовуються у наші дні. Такі архаїчні вирази або були зовсім випущені, або ж замінені сучаснішими еквівалентами. Багато речень-прикладів були додані до тих, що вже були у оригінальних посібниках. \nWith the permission of Cora E. Anderson (Canada), who is the author of the workbooks : “Easy Approach to English Idioms and Idiomatic Expressions” (in two volumes), we have reworked her original books to make a version suitable for Ukrainian high school and university students. The resulting book has a Ukrainian translation for each English expression. The entries are the same given in Cora Anderson’s workbooks, with the exception of a few expressions that are not used much in the present day. Such “archaic” expressions have either been dropped altogether or replaced by a more modern equivalent. Many example sentences have been added to those already in the original workbooks.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".