STEM Education in Canada and Ukraine: Transformations, Innovations, and Pathways for Sustainable Development
Bibliographic record
Abstract
Why This Book Appeared at This MomentThis volume emerges at a time when education systems worldwide are undergoing profound transformations.Questions of how to prepare societies for an era defined by artificial intelligence, climate change, and digital interdependence have never been more pressing.STEM science, technology, engineering, and mathematics has long evolved beyond the boundaries of individual disciplines.It now represents a framework through which nations define their innovation capacity, competitiveness, and sustainability.STEM Education in Canada and Ukraine: Transformations, Innovations, and Pathways for Sustainable Development is therefore more than a collective academic study.It is a bridge connecting two educational systems that, though shaped by very different historical experiences, share a commitment to progress through knowledge, creativity, and technology. Relevance and FocusUkraine today stands at a crossroads of reconstruction and renewal.Its future depends on the ability to rebuild infrastructure and human capital through innovation and education.A strong STEM foundation is essential for this task, encompassing areas such as energy, biotechnology, cybersecurity, and modern Together, these contributions affirm a shared belief: that quality, innovative, and socially responsible education in science and technology is the surest path toward a sustainable and human-centered future.The third chapter focuses on STEM-oriented teacher preparation.H. Rozlutska, S. Hirniak, and Ya.Petryna substantiate the relevance of integrating STEM elements into the professional education of future primary school teachers, presenting experimental results that demonstrate improved readiness to apply innovative teaching methods.A. Yurchenko's study reveals how the integration of project-based learning and digital visualization fosters critical thinking, teamwork, and
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".