Bibliographic record
Abstract
Traditional investment in Ray assumes that investment takes place in a safe environment, while in many countries, including Iran, there is a high degree of uncertainty.Growth, inflation, exchange rates and other economic variables macro-economy of the industrialized countries are more prone to fluctuations.fluctuations in inflation and uncertain environment for investment decision-makers, while most fundamental concept in evaluating the financial condition and results measurement related to organizational performance, the investment in the organization .From this perspective, the realization of real benefit to the creation of a surplus means that harvesting and non-productive consumption does not create a dent in the capital.Because of any business organization pay an annual fee as the share of profits to be, loan interest and the income tax to the owners of investors, creditors and government officials pay, determine the amount of the capital gains or return of capital distribution is crucial.Business organizations and sustainable response to the regular changes of a variable can be done at a lower cost and more reliably.But when these changes are unstable and irregular form, they create uncertainty that under these circumstances, economic decisions risk and cost will be greater.Therefore, how to control them identify the trend of economic variables and how, to mitigate their impact on other economic variables is of particular importance.Inflation and its impact on capital including the issues is.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.976 | 0.975 |
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".