Optimum population distribution described by dynamic models and controlled by immigration and job creation
Bibliographic record
Abstract
In this thesis, dynamic mathematical models are constructed to describe the population distribution in Canada based on the model in previous work by Ahmed and Rahim [1]. Numerical results demonstrate that the model population is in close agreement with the actual population. This indicates that the presented model can be used as a valuable tool for describing the dynamics of population distribution. We also demonstrate that by using modern Systems and Optimal Control theory [2], it is possible to formulate optimum immigration and job creation strategies while maintaining population level close to certain pre-specified targets. An optimization algorithm [2] is then developed based on dynamic programming and gradient algorithm approach. Unknown parameters such as birth rate, death rates and transition rates are estimated and identified. The system model obtained by using the identified parameters is then augmented by adding a fourth equation describing the dynamics of unemployment rate. This model is then used to formulate a control problem with immigration and job creation rates being the decision (control) variables. Using optimal control theory, optimum immigration and job creation policies are determined. Results are illustrated by numerical simulation and they are found to be very encouraging.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".