Grappling with Governance: The Emergence of Business Improvement Districts in a National Capital
Bibliographic record
Abstract
Business improvement districts (BIDs) constitute a relatively new mode of urban governance in which business and property owners pay surtaxes for collectivized, privatized maintenance and development services in their respective neighborhoods. Although they are typically considered an innovative means of improving urban areas—or at the very least a benign intervention of business owners to draw new consumers—the case of Washington, D.C., shows that BIDs are also an increasingly entrenched neo-liberal institution promoted by state restructuring and interurban competition. Given local conditions, such as a permissive legislative environment and fragmented governance, the proliferation, size, and influence of D.C.’s BIDs pose concerns about institutional accountability, socioeconomic inequality, and sustainability of services. Using a mixed-methods approach that integrates urban governance theory, performance metrics, and interviews with BID and D.C. government officials, this study finds that Washington’s BIDs have promoted revitalization but also pose concerns about limited public accountability, exacerbated socioeconomic and spatial inequalities, and further retreat of the municipal government.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".