MétaCan
Menu
← Retour à la cohorte
Enregistrement W1569448263

Homelessness as an Impediment to Urban Revitalization: the Case of Dallas, Texas

2004· preprint· en· W1569448263 sur OpenAlexaboutno aff
Bernard L. Weinstein, Terry L. Clower

Notice bibliographique

RevueEconstor (Econstor) · 2004
Typepreprint
Langueen
DomaineHealth Professions
ThématiqueHomelessness and Social Issues
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDowntownCensusValuation (finance)Quarter (Canadian coin)GeographyBusinessSocioeconomicsEconomic growthFinanceSociologyDemographyEconomicsPopulation
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Homelessness has long been recognized as a serious problem in many American cities, and Dallas in no exception. What’s more, the homeless tend to congregate in the downtown districts (DD) since most service providers are also located in the urban core. Though homelessness is typically considered a social problem, it also has economic consequences. The latest homeless census for the city of Dallas totaled 6,000, and annual outlays by governmental, non-profit, charitable, and faith-based organizations to provide them with services probably exceed $50 million. This estimate doesn’t include thousands of volunteer hours. But the true economic cost of homelessness is much greater. A survey of downtown business owners found that the presence of homeless persons is having a negative affect on their operations and burdening many of them with additional costs for security and cleaning. A majority of retail respondents report that proximity to the homeless was scaring off customers and reducing their sales. An examination of downtown properties using Dallas County Appraisal District (DCAD) records reveals that average values in the southern sector, where most of the homeless are concentrated, are well below those in the northern half of downtown. Consequently, the City of Dallas, Dallas County, and the Dallas Independent School District are losing $2.4 million per year due to valuation disparities from a lack of development in the southern half of the DD. What’s more, we estimate the southern half of downtown can potentially support almost 2.2 million square feet of additional commercial, office and residential space. This development scenario would create more than 5,000 new jobs and generate about $6.6 million per year for local taxing entities. But the revitalization of Dallas’ DD, an avowed goal of the city’s political and business leaders, will not be fully realized until a comprehensive plan for improving homeless services is developed and implemented. Most importantly, the proposed central intake facility should be located away from—but close to—the downtown district. In this regard, the City of Miami can serve as a model. Miami has significantly reduced the visible homeless count and greatly improved the delivery of services. By creating an umbrella agency to oversee all homeless programs—whether provided by government, voluntary or faith-based institutions—the city has avoided duplication and overlap of services. Significantly, Miami has located both of its central intake facilities, known as Homeless Assistance Centers (HACs), away from their downtown district. Miami’s businesses community has recognized that reducing homelessness is a community and economic development issue as well as a social problem, and to that end they have contributed about $50 million over the past decade. The results are tangible, as evidenced by the construction boom currently underway in Miami’s downtown. As with Miami, an effective approach for dealing with Dallas’ homeless population must include greater participation and support by the region’s business leaders. Homelessness has significant economic as well as social consequences for the City of Dallas. While offering our compassion to the homeless, we should also acknowledge that the overwhelming presence of homeless persons on the streets of downtown has negative economic impacts on individual businesses, the prospects for redevelopment, and the city’s finances.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,288
Score d'incertitude au seuil0,573

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0180,006
Communication savante0,0060,003
Science ouverte0,0020,007
Intégrité de la recherche0,0030,006
Charge utile insuffisante (le modèle a refusé de juger)0,0080,000

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.

Tête enseignante Opus0,034
Tête enseignante GPT0,370
Écart entre enseignants0,336 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2004
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueEconstor (Econstor)→Même sujetHomelessness and Social Issues→Travaux en français237 207→