Monetizing Flint's Vacant Facades: Public Art and Advertisement Auctions as Blight Remediation
Notice bibliographique
Résumé
Flint, Michigan, is a city that has weathered decades of population decline, economic hardship, and disinvestment, and faces a pervasive blight challenge. Recent assessments show that about 8,500 residential properties (roughly one quarter of all homes) and more than 900 commercial parcels, nearly half of the city's commercial properties, are vacant or derelict. (Bennett and Holloway, 2022). These abandoned structures depress surrounding property values, invite vandalism and crime, and strain public resources for maintenance and safety. Amid this challenge lies an opportunity to turn liabilities into assets. This paper presents an innovative proposal to transform Flint's many derelict building facades into platforms for public art and paid advertisements, with the generated revenue directly financing further blight remediation. Property owners or the City (for publicly owned buildings) would allow the exterior walls of structurally sound, high-visibility vacant buildings to be used for large murals or billboard-style displays. The rights to each facade would be allocated through a competitive auction open to advertisers and art sponsors. Winning bidders gain the right to install and display content (artwork, commercial advertising, or a combination) on a facade for a fixed term (up to about two years), after which the facade is again up for bid. By cycling through auctions every couple of years, the visual landscape remains dynamic and periodically refreshed, preventing any single installation from becoming dilapidated or stale. At its core, this project reinvests the proceeds from these facade auctions into the very properties and neighborhoods generating them, the forward-facing parts of Flint's urban landscape. Research from multiple case studies underscores the potential of such creative placemaking initiatives to drive substantial economic and social benefits (Project for Public Spaces, 2008). Moreover, broad evidence shows the arts can yield significant economic returns. In 2022, nonprofit arts and culture organizations and their audiences generated an estimated $151.7 billion in economic activity across the United States, supporting 2.6 million jobs and producing $29.1 billion in tax revenue (Brancaccio and Schroeder, 2023). Governments have taken note: states such as Minnesota, Florida, and Illinois have implemented "percent for art" programs that allocate a portion of public infrastructure budgets to public art, demonstrating confidence in the financial viability of art-driven revitalization (NASAA). Public support for such investments is likewise strong. National surveys indicate that 55% of Americans favor increasing federal arts funding, 57% favor more state arts funding, and 58% favor more local arts funding (Delaware Arts Alliance, n.d.). Within this context, the proposed facade monetization program offers a self-sustaining blight remediation mechanism that leverages private sector marketing dollars and philanthropic art sponsorships, rather than relying solely on grants or limited city budgets. The hypothesis is that a facade auction program can simultaneously achieve urban beautification, community engagement, and economic revitalization.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».