Evaluation of Financial and Innovative Potential of the Commercial Organization based on the Definition of Financial Innovation Sustainability
Bibliographic record
Abstract
Theoretical and methodological researches show that there is no single opinion about approaches to estimating innovative capacity of economic entities. The work studies characteristics of innovative capacity within the resource-based approach. This approach reflects opportunities for innovation activity development, defines the strategy of innovational development, and combines innovation capacity with a specific level that identifies innovative capacity with scientific and technical, technological level. Through the theoretical and methodological standpoint it offers the approach to defining the essence of innovative capacity, generalizes classification of its types and kinds according to specific classification criteria, and systemizes the approaches to estimating innovative capacity. These are the following approaches: detailed approach, diagnostic approach, the approach based on estimating financial and innovational soundness of the organization and separate methods. In practice the innovative capacity of Russia was estimated by calculating the global index of innovations in comparison with other countries of the world according to the level of innovation opportunities and results. On the macro level the priority of the approach based on estimating financial and innovational soundness of the organization was rationalized. This approach is meant both for adequate estimation of its state and readiness for innovations implementation. As a result, key areas of using results of estimating innovative capacity on the macro, meso and micro levels were defined.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".