Bibliographic record
Abstract
This study addresses the relationship between economics and mathematics, drawing attention to the fact that although economics is a social science, mathematics plays an important role in understanding economic processes. Due to the complexity of human behavior, it isn't easy to achieve mathematical precision in economics. However, thanks to mathematical tools such as econometrics and modeling, it is possible to plan, predict, and analyze the relationships between economic variables. Therefore, using of mathematics in economics is necessary. It is stated that correlations should be understood in understanding the relationships between economic activities and the extent of the relationships. The development of regression models is emphasized in predicting future trends and supporting decision-making processes. However, the difficulties economists face when using advanced mathematical techniques are mentioned. Despite some of the difficulties, risk, and uncertainty conditions mentioned, it is emphasized that mathematical or econometric analyses continue to be important for planning and making consistent estimates and that some conveniences have been experienced with technological developments. As a result, it is stated that a balanced approach is needed in using mathematical tools in economics. In other words, it is stated that models, which are merely tools rather than goals for economic analysis, have limitations and that it is desired to benefit from the prediction and consistency of these tools. Additionally, it is suggested that future education should both update and follow the analysis tools offered by technology and place more emphasis on mathematical and econometric knowledge to develop the ability to better predict uncertainties.
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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".