Redlining in Prince George’s County, Maryland
Bibliographic record
Abstract
The project goal was to provide the Prince George’s Planning Department with geographic information regarding historical redlining in the County. Redlining is the act of denying a person the ability to buy property or a house within a specific area due to their race or ethnicity. This project allowed us to understand the impact that redlining has on Prince George’s County. In PGAtlas.com, we obtained county addresses and their associated plat numbers to look up subdivision plats. We scanned property deeds that accompany subdivision plats to see if there were any deed restrictions that might indicate redlining. We compiled a data table of 15 redlined addresses in the County and provided map entries for a story map on the ArcGis story map. The story map helps show how certain areas in the County were targeted with redlining in the 1890s through the 1940s. In addition, the story map outlines the history of redlining in other areas and how Prince George’s County is one of many communities to have been affected. Our contacts for the project were Prince George’s County Planning Department staff, Dr. Jennifer Stabler and Karen Mierow. We also worked with Kimberly Fisher and Lily Murnen of the Partnership for Action Learning in Sustainability (PALS) program. Our project required understanding both clients’ goals and objectives to achieve a final product agreed on by both parties.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".