Enhancing Equity and Economic Growth: A Strategic Review of Procurement Practices in Prince George’s County
Bibliographic record
Abstract
Procurement represents a substantial portion of public spending in state and local governments, and thus is an essential element of responsible public financial management. This paper explores the procurement process on the local government level and provides research into best practices that are conducive to equity and economic development. It takes a look specifically at Prince George’s County, Maryland and analyzes its procurement function in relation to these best practices. An analysis of the County finds that despite its strides in implementing equitable procurement policies, significant gaps remain in the engagement of disadvantaged classes of businesses. To tackle these challenges, the paper proposes several key recommendations: modernizing data collection and reporting systems to boost transparency and accountability; enhancing monitoring and enforcement mechanisms to ensure adherence to equity goals; and improving outreach efforts to bolster vendor participation. Furthermore, it recommends lowering bonding requirements to alleviate financial barriers for small businesses, standardizing methods for assessing “good faith” efforts by subcontractors and conducting regular disparity studies to keep an updated view on equity in procurement. By implementing these recommendations, Prince George’s County can establish a more inclusive and effective procurement framework that supports disadvantaged businesses and fosters fair competition.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".