Improving the Methodological Support for Recording Expenditures and Outcomes of R&D
Bibliographic record
Abstract
At present, the evaluation of the results of innovation is important when choosing promising investment projects for all levels of management—from economic entities interested in the implementation of innovative strategies to federal authorities responsible for science, technology and innovation policies in the country. At the same time, great importance in our country is given to an innovative way of development of enterprises that perform research and development activities (R&D), as evidenced by the increase of the share of innovative research nearly 2.5 times over the last five years. Successful intensification of R&D process is due to the creation of innovation centers: “Skolkovo”, “Technopark-Sarov”, Technopark of Novosibirsk Akademgorodok, “Technopark-Strogino” and others that are designed to maximize territorial cohesion of science, industry and commerce. The existing regulatory framework for R&D accounting does not allow to defining a clear distinction between “research” and “development” and therefore regulates only the end result of these activities. This leads to a distortion in the formation of expenditures for R&D and unreliable monetary terms relating to their outcomes. In addition, in the current practice, the problems of application of international accounting standards still remain open, which adversely affects the attraction of foreign investors. As a solution to the existing problems, it is necessary to use the fundamental principle of national accounting) time definiteness of the facts of expenditures and outcomes of R&D—when making professional judgment. The unresolved problems in the recognition of expenditures and outcomes of R&D in accounting and reporting result in an increased interest in conducting special studies to find effective methodological support for innovation management that meets the different needs of users of financial statements.
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 | 0.246 | 0.498 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".