Analysis of Basic Education Concepts in Ukraine and Canada (in the Field “Advertising and Public Relations”)
Bibliographic record
Abstract
The aim of the article is to conduct a comparative analysis of basic research concepts in the Ukrainian and Canadian scientific space related to the concepts that characterize the general context of professional training, in particular in the field of advertising and public relations. The research methodology is based on general scientific and terminological methods, the comparative method. As a result, the comparison of the basic research concepts in the Ukrainian and Canadian scientific space are considered, namely, education, professional education, vocational education, continuing education, lifelong learning, competence, profession, trade, professional training, professional competence, advertising education, integral competence of a specialist in advertising and public relations, general competencies of a specialist in advertising and public relations, etc. The results of the research, first of all, can be used in systematization of the accumulated scientific knowledge in the field, synthesis and generalization of scientific achievements of the field, ensuring the successful application of scientific achievements in practice. Conclusions. The comparative analysis of basic research concepts in Ukraine and Canada that characterize the general context of professional training, in particular, in the field of advertising and public relations is done. The experience can be taken into account when reforming education in Ukraine and Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".