Educational Union secondary school – higher technical school – As a Way To European Standards of Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Peculiarities of educational systems of China, Australia, Brazil, Canada, the USA, the European Union, the former Soviet Union, the Russian Federation, Ukraine. Ukraine’s integration into the EU involves knowing the English language. In lyceum "Intellect" 12 subjects are taught in English. Three characteristic features of educational process in the lyceum are: who teaches, how it is done, what textbooks and what syllabus are used. Four levels of a lyceum graduate. Conducting experimental research work on the topic "Creating a model of innovative educational unit association "Secondary school – higher technical school" as a way to European standards of education” on the basis of Kyiv lyceum "Intellect" and Polytechnics University KPI. Subject "Technologies" is a peak of a pyramid, based on physics, mathematics, humanities, the English language and science. The task of Linguistic education and European language portfolio.In his article the author introduces a unique system of work of Kyiv private lyceum "Intellect". He focuses on three key principles of a successful educational process: an extensive usage of modern technologies, a unique syllabus (based on the original books compiled by the teachers of the lyceum) and the team of talented and experienced tutors. Furthermore, the author specifies the peculiarities of teaching physics and mathematics, the system of evaluating pupils’ progress and offers some recommendations regarding the improvement of secondary school activity under the requirements of the present day realities.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it