EVALUATING IMPACT: THE ALBERTA MAIN STREET PROGRAM’S EFFECT ON PROSPERITY, VIBRANCY, AND EQUITY IN PORTLAND, OREGON
Bibliographic record
Abstract
The Main Street Program is one major policy tool used to meet the City of Portland’s goal to create prosperous, equitable, and vibrant neighborhoods. Literature reveals that existing evaluation of the Main Street Program at local, state, and national levels focuses solely on changes in prosperity such as dollars invested in physical improvements. This project defines metrics to evaluate changes in equity and vibrancy then applies these metrics to the Alberta Main Street Program. Metrics to evaluate vibrancy focus on changes to the built environment while equity metrics evaluate socioeconomic changes. Research was conducted through a longitudinal study of NE Alberta St from the start of the program in 2010 to 2016. Methods include collecting field observations, analyzing GIS data, analyzing U.S. Census data, and interviews with business owners. Findings indicate positive changes to vibrancy such as increases in active uses of space, good physical maintenance, and presence of street element details. Additional findings show there has not been a positive effect on equity due to the concurrent large residential displacement of historically marginalized people and inequitable access for resources to business owners. The City of Portland claims the Main Street Program is a positive force for equity, but this project sheds light on the lack of evaluation to support these claims. Future research should focus on if the Main Street Program should be used to meet equity goals or if other programs would be more suitable.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".