Fiscal Countermeasures of Population Aging for Economic Development Based on Big Data Algorithm
Bibliographic record
Abstract
Like money or gold, data has emerged as a new class of economic commodity.Big data is now a factor of production on par with other material resources, having permeated every aspect of today's economy and society.Social development inevitably leads to population aging, which affects all facets of social life, particularly social and economic development.Nevertheless, systematic and thorough study on how population aging affects economic development is still lacking.The economic and iscal policy trade-offs of aging on economic growth are the main emphasis of this article, which is based on big data techniques.This study examines the effects of population aging on economic development from the perspectives of economic growth, social security, and inancial pension expenses, based on an analysis of the current state of population aging and its drivers.It was designed to address the aging of province A's population and discovered that it not only caused the share of the working-age population to decrease, but also decreased the resources available to the labor force.The proportion of tax revenue in total iscal revenue will continue to be over 82% by 2021, with 73% of the population being between the ages of 15 and 64.The scale of iscal pension expenditures in Province A has shown a clear upward trend.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".