The Discussion on Input – Output Framework Extended for Analyzing on Relationship between Demographic and Economic
Bibliographic record
Abstract
So far, many studies on economic structure have been proposed, Studies on the relationship between demographics and communication economics basically consider changes in age structure, leading to changes in saving and investment capacity. In Miyazawa's demographic-economic model, the focus has been on quantifying the relationship of final consumer groups and corresponding income groups. This study tries to establish the relationship between age and output and income. This study tries an attempt to extended Miyazawa’s model which gross capital formation at columns and operating surplus at rows. That means the input – output system was not only extended aging group at consumption of employees at rows and final household consumption at columns, but alsoadd to gross capital formation at columns and total income of producers (operating surplus and exogenous income) at rows. In this system, it is allowed to consider changing population structure which not only affects economy through saving or investment but also the structure of final consumption by age also spreads to output and income. So, in this research is not only this related inter-sartorial at first-time distribution for considering but also the impact of demographic to economic activities and re-distribution income follow by type of aging group.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 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; 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".