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Winter Places 2.0: A Design Guide and Report on Winter Placemaking

2021· report· en· W7071984194 sur OpenAlexaboutno aff

Notice bibliographique

RevueIssue Lab (Candid) · 2021
Typereport
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPlacemakingGlobeSAFERPlan (archaeology)Space (punctuation)Public spaceLocal community
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The past two years have presented a series of unimaginable financial, social and health challenges for communities across the globe. A continuing global pandemic forced the closures of businesses, the physical separation of people and all of us to grapple with the idea that outdoors was safer than indoors with a virus spread through the air around us. While none of us had ever lived through something like this before, communities across the country and around the globe got to work, supporting their communities by opening streets, granting permission to replace street parking with dining setups, establishing shared community dining spaces and much more. Creative projects were now embraced by public and private sector in a bid to keep people safe while allowing their communities to find opportunities to stay socially connected and local businesses to remain open and solvent throughout the pandemic.The winter of 2020 presented a unique set of challenges, one that required even more planning and support than the summer and fall of 2020. With the virus still raging cold weather communities had to think quickly to figure out ways to provide their communities a safe, outdoor, inviting space to connect with friends and neighbors while also providing the opportunity to support local businesses through what is already one of the more difficult times of the year. In the fall of 2020 we released a Winter Places Guide with creative concepts and ideas aimed at supporting communities around the world in their efforts to get people outdoors in their towns, supporting local small businesses in the process. The guide was downloaded over 3,000 times by individuals and organizations from across the US, Canada, Europe and Asia and we heard incredible feedback from Mayors, Main Street Directors, Town Planners, residents and artists inspired to get their communities outdoors in the winter months.Thanks to a funding partnership with Boston based Barr Foundation, we were able to support and fund twelve community winter placemaking projects across Massachusetts that were inspired by ideas in the Winter Places guide. These placemaking projects were led by some incredible cross-sector, public/private partnerships within each community and were designed, implemented and programmed alongside area businesses, residents and with the support and guidance of local boards of health. Though many delays were encountered based upon local Covid-19 conditions on the ground in each community, each project drew hundreds, if not thousands of area residents into the local commercial districts, giving residents a space to gather safely and businesses the opportunity to have foot traffic again during a traditionally difficult time of year, made even more difficult by the pandemic related restrictions. Nearly 100 local artists and craftspeople were employed for the implementation of these projects and close to 250 Massachusetts small businesses participated directly in this winter programming experience.The following guide includes detailed project reports on each campaign funded across Massachusetts as part of our program with Barr Foundation, resources produced during last years Winter Places program and information on how you can get support to activate your community during the coming winter. We hope this guide serves not only to inspire you to embrace winter outdoors in your community but provides practical tips to help get you there as we continue to work together to find creative ways to build community, foster new relationships and support our local economies in a changing pandemic environment.

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)0,003
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,133
Score d'incertitude au seuil0,444

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,007
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0020,001
Communication savante0,0060,004
Science ouverte0,0030,004
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,1330,068

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 Opus0,054
Tête enseignante GPT0,345
Écart entre enseignants0,291 · 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'étudeSans objet
Domainenon disponible
GenreAutre

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

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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