Models for Estimating Intrinsic r and the Mean Age of a Population at Stability: Evaluations at the National and Sub-national Level
Bibliographic record
Abstract
Abstract Using Canada’s provinces and territories in conjunction with the “Cohort Change Ratio” approach to generating a stable population, I test the accuracy of two regression models constructed from national-level data designed to estimate two factors of a population at stability from initial conditions at the sub-national levels: (1) its constant rate of change, denoted here by r'; and (2) mean population age. In a test of accuracy at the national level I find that these models provide reasonably accurate estimates. In the tests at the subnational level, the accuracy, as expected, is less, but the results indicate that the national level models provide estimates that are useful. The models are useful because they are tractable and provide information not available from the traditional analytical approaches. Evaluating these models also provides the opportunity to look at Canada’s provinces and territories from a stable population perspective. The findings support the use of: (1) The Cohort Change Ratio approach in examining stable population concepts; and (2) the two regression models for estimating r' and the mean age of a population at stability. They also show that there are connections between initial conditions and stability that have been overlooked. This knowledge gap may be due to the fact that widespread knowledge and acceptance of the ergodic nature of the “age structure factor,” have served to mask the possibility that ergodicity does not always apply to other factors. Further exploration of these potential linkages appears to be in order.
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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 | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".