Home Ownership in City Core Neighborhoods
Bibliographic record
Abstract
This analysis employs a traditional methodology to reproduce American Community Survey (ACS) data as those data would be if neighborhoods within the city of Kalamazoo were recognized by the U.S. Census Bureau. By aggregating publicly available ACS information from recognized census geographies, rates of home ownership and home values within three city neighborhoods are estimated. Home ownership rates by race are approximated. Rates of homeownership in the Eastside neighborhood are similar to those across Kalamazoo County and the state of Michigan. While all three neighborhoods have lower average values of owned homes relative to their county and state neighbors, home values in the Edison neighborhood are higher on average than those in the other analyzed neighborhoods. Although home ownership rates and home values show signs of being lower in the Northside neighborhood relative to the other analyzed neighborhoods, these differences could not be confirmed statistically.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.002 |
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; both teacher heads agree on what is shown here.
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".