Development of the Intercensal Estimates of Population and Housing, 2000-2010
Bibliographic record
Abstract
Intercensal estimates are estimates developed between census years. Intercensal estimates are considered more accurate than other types of estimates because they are bracketed on both sides by decennial census counts or state-certified special census counts. The intercensal estimates developed by the Office of Financial Management (OFM) are based on the housing unit method. The housing unit method assumes that the change in the number people varies with the change in number of housing units and counts of population living in group quarter facilities, as reported to OFM by local governments and institutions over the decade. The basic assumptions of model are as follows: • The federal decennial census counts for 2000 1 and 2010 1,2 are correct. • State certified special census counts are assumed to be correct for any given year in the series. • The OFM estimated populations for new incorporations are assumed to be correct 3. • The change in household population change mirrors the pattern of housing change as reported to OFM by counties, cities and towns each year. • The change in group quarter population 4 mirrors the change in population counts as reported to OFM annually.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".