Housing as a Heuristic Condition in the Simultaneous Projection of Population and Households
Bibliographic record
Abstract
Conventional population projections regard individuals, rather than households, as population units of reference. Such an approach has been questioned on both methodological and empirical grounds. Furthermore, in applications to smaller populations, conventional population projections have repeatedly yielded poor results. The simultaneous projection of population and households, on the other hand, regards households as population units of reference, but, in applications based on the notion of the household composition matrix, it has occasionally yielded analytically infeasible results. In the present study I examine the simultaneous projection of population and households in a etropolitan area, under feasibility constraints. A housing-market specification is expressed as a feasibility condition against multipliers of the household composition matrix, extracted here for the Cleveland Consolidated Metropolitan Statistical Area (CMSA), 1990. The feasibility condition is shown to function as a gateway to exogenous considerations regarding the transfer of headship in households, and is exemplified in a forecast of population and households for the Cleveland CMSA.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".