Educating the Reputation Capital Impact of a Region on the Parameters of Its Investment Activity: Methodical Approaches
Bibliographic record
Abstract
As a practice and empirical observations show, the educational activity of economic agents forms a whole set of prospects for their development. It is due to stable positive or, on the contrary, negative expectations of prospective counterparts that interact with the economic agents, of the possibilities of their development in both the conjuncture and the institutional directions. In this regard, education is very important, and its study is becoming widespread in the research field. Meanwhile, it should be noted that the studies of the question posed in the space of scientific work and research mainly concentrate on the micro-level. That is, the vast majority of works are devoted to the study of the education of firms as one of the most important "representatives" of economic agents. At the same time, the study of educational capital and its influence on the development of regional / national economic systems is unfairly deprived of attention. It is necessary to state that in recent years, the attention of scientists began to focus more and more on the issues set earlier. However, the theory of the territories' educational economy has not yet received development and attention. This largely restrains the research paradigm based on the study of intangible factors of production in the system of socio-economic development of regions or national economic systems in general.In this regard, and to level this gap, the time course of the reputation capital index for the Republic of Tatarstan and the main components determining its level are built in this paper based on the developed methodological approaches to a formalized assessment of the reputation of a territory. This allowed us to assess the reputation impact on the investment activity of a region using the methods of econometric modelling. In particular, being guided by the principles and tools of regression analysis, as well as relying on the method of dummy variables, a high level of interconnection between the studied parameters was established by the authors due to the significant level of elasticity between the analyzed series found.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".