SMART Technologies as the Innovative Way of Development and the Answer to Challenges of Modern Time
Bibliographic record
Abstract
One of the promising tasks in education lies in reforming it into the knowledge economy, integrating and creating a market oriented towards results of intellectual activity. On the other hand, globalization process requires transition of the educational environment to the format of information, communication and digital space. Primarily these areas are set as the nodal tasks, which directs authors of this article to the comparative analysis of educational system making it possible to identify general and particular, positive or negative consequences and characteristics of digitalization in the higher education system. In accordance with current trends and processes of globalization and informatization, the authors are considering the prospects for interaction and mutual influence of Smart technologies used in building a future educational model in the higher education area. Technological innovations today are called upon not only to qualitatively change methods, forms and technologies in the education content, but rather to train personnel capable of operating in the new information and telecommunication community. Therefore, studying the influence and the capabilities of modern digital technologies that meet needs of society, on the one hand, and, on the other hand, contribute to formation of professional competencies in students, which requires major alterations in the learning process, changes in its state towards flexibility, adaptation, personalization, continuity, multidimensionality and systematicity, becomes of specific relevance for authors of this article.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".