Bibliographic record
Abstract
Chapter 1: Interregional Transfers through Public Pension in Canada- In this chapter, I build a regional computable general equilibrium model with an overlapping generations (OLG) structure of the Canadian economy to analyze population aging dynamics and public pensions. Canada is divided into three regions: Atlantic, Quebec and Rest of Canada (ROC). The impact of population aging is investigated on each of three regions' pension systems. The results confirm that as a result of aging all regions are affected negatively if they choose to have an independent pension system. Under a joint pension system most of the pressure of the provision of the pension system is on the ROC. Atlantic region benefits the most from a joint pension plan as the implicit funds ow from ROC to Atlantic region. Quebec benefits from having its own program, but the benefits disappear slowly in future years. Chapter 2: Age-Variable Rate of Time Preference in CGE-OLG Model- Contrary to the mainstream studies in the area of intertemporal optimization that assume a constant rate of time preference over individuals' life cycles, in this chapter I propose a new approach to measure the rate of time preference by assuming that the rate of time preference evolves by age. I construct an overlapping generations model (OLG) and calibrate rate of time preference. The age-variable rate of time preference would permit to capture many other elements that affect the life cycle profile of consumption as observed in the data. The results show that rate of time preference exhibits three phases and is different for young versus old. Chapter 3: Computing Demographic Change Simulation under Constant and Age-variable Rate of Time Preference - This chapter simulates the impact of an aging population on various macroeconomic variables and calculates the cohort welfare as well as social welfare. The outcomes from simulations are dependent on the choice of rate of time preference as well as the structure of the model. The results in this chapter provide a new approach to determining the impact of aging population. The choice of a realistic rate of time preference, which allows its variability by age, affects the cohort welfare noticeably.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".