The term «Social Economy»: essence, concept, international identification
Bibliographic record
Abstract
Целью данной статьи является оценка использования термина «социальная экономика», с учетом отсутствия у него конкретности. Начиная с 2004 года и в связи с Хартией принципов социальной экономики, началась предметная разработка данного термина в научной литературе. Странами, где которые наиболее часто употребляют данное понятия, являются Испания, США, Китай, Великобритания и Канада. Социальной экономики присущи такие направления, как: устойчивое развитие, изменение климата, урбанизация, управление. В статье показано, что социальная экономика воспринимается как пионер нового видения богатства, ориентированного на людей и их среду. Несмотря на это, авторы отмечают, что исследования и анализ предмета «социальная экономика» продолжаются, изучается ее научное, политическое, юридическое и экономическое содержание. The purpose of this article is to evaluate the use of the term «social economy», while recognizing its lack of specificity. Since 2004, and in connection with the Charter of the Principles of Social Economy, the material development of this term in the scientific literature has begun. The countries that most often use this concept are Spain, the USA, China, the UK and Canada. The social economy is characterized by such areas as sustainable development, climate change, urbanization, management. The article shows that the social economy is perceived as a pioneer of a new vision of wealth, focused on people and their environment. Despite this, the authors show that research and analysis of the subject of social economics continues, studying it in conjunction with scientific, political, legal and economic content.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".