Substantive, methodological and organizational discourse in oriental history learning at school and university
Bibliographic record
Abstract
© 2015, Canadian Center of Science and Education. All rights reserved. Currently, the system of education in Russia is changing radically. One of the factors behind the process of education reorganization, university education in particular, is the process of globalization and computerization. Advanced concepts and the best practices of market-leading educational services (especially in the US and the UK) made it possible to develop the national education model with the aim to solve a number of problems related to the formation of a modern education model, historical education including, such as future demand for specialists, efficiency of the knowledge obtained, and mobility of professional qualifications under present conditions. The article focuses on the issues of training history teachers at universities as well as the issues related to teaching history, especially world history, at schools. The authors discuss relevant issues concerning the ways of improving methodology and technologies in history teachers’ school practice, analyze the methods for increasing motivation of history students through implementation of innovative educational technologies, and outline the guidelines for intensifying students’ learning activities in preparation for their final exam in history.
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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.024 | 0.112 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".