MicSim, a microsimulation model for population dynamics
Bibliographic record
Abstract
Abstract Demographers use microsimulation for studying individual life courses and their attributes; events are specified to be the result of stochastic processes based on predetermined probabilistic rules. In this study, we developed and validated a microsimulation model to reconstruct individual’s life courses and their interactions in different regions of the European Union. One of the main objectives of this study was to track migrants’ pathways in the context of three population systems, namely Sweden, the Netherlands and Spain, from 2014 to 2018. We used official datasets as data inputs to analyze and project future population dynamics. A revised version of the MicSim package which is part of the statistical software R has been used to model mobility and migration patterns at large scale. For this purpose, among other things new functions were added to make the software more efficient concerning runtimes and data handling. By analyzing the modelling results we conclude that MicSim has the potential to be applied for modelling migration and also more general population movements at a large scale. The application of the MicSim package would provide policy makers with a valid instrument for the governance of migration accounting for the demographic and social patterns of migrants and their origin-destination contextual environments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".