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Enregistrement W2302400934 · doi:10.14288/1.0075861

Connecting immigrant communities to local government : the case of Richmond, BC

2015· article· en· W2302400934 sur OpenAlexaboutno aff
Eliana Chia

Notice bibliographique

RevuecIRcle (University of British Columbia) · 2015
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueMigration, Ethnicity, and Economy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésImmigrationGovernment (linguistics)Local governmentPolitical scienceDeportationPublic administrationLaw

Résumé

récupéré en direct d'OpenAlex

Richmond, British Columbia stands out amongst Canadian cities by having one of the nationally highest concentrations of immigrants in its population, as 62% of Richmond’s residents are first generation immigrants. Although immigrants from China and Hong Kong constitute a significant percentage of overall immigrants to Richmond, in the last 8 years, there have been notable numbers immigrating from the Philippines, Taiwan, and India. Along with the high population of immigrants comes the challenge of engaging newcomers as well as non-English speakers in unfamiliar City government processes. In response to the identified needs and recommended actions from the 2013- 2022 Social Development Strategy as well as the 2012-2015 Richmond Intercultural Strategic Plan, the Connecting Immigrant Communities to Local Government project was created. This project attempts to answer: How can the City of Richmond support immigrant civic engagement? Civic engagement is defined as knowledge about civic processes and the capacity to participate in local planning and governance. The project reached out to three different groups of stakeholders to receive input about opportunities to improve immigrant civic engagement. These three groups included City of Richmond staff, staff from immigrant-serving community organizations, and immigrant residents in Richmond. In Richmond, the non-profit and public sector offer various programs that focus on sharing information with newcomers about how City Hall and other levels of government function. There are also a few initiatives that try to strengthen connections between immigrants and City Hall. The gap that exists in Richmond is a long-term educational program that provides consistent support and training for immigrants on how to engage with their City and community at a decision-making level. Immigrant residents face multiple barriers when interacting with City Hall. The primary barrier is language, as many newcomers have limited English language skills and City staff may be using high-level, professional means of communication. The Canadian government’s federal structure can be confusing for both newcomers and established immigrants, and the City’s consultation processes can also be intimidating for people who do not regularly interact with City Hall. Immigrants from certain countries may have a strong mistrust of government due to their former experiences with more authoritative governance systems. For some immigrants, City Hall may not be a welcoming space due to a perceived lack of staff and citizen participants from their racial background. City staff also face various challenges when they are attempting to engage immigrant residents in their planning processes. Staff struggle with providing appropriate translation due to a lack of resources and clear corporate guidelines. Staff also face difficulties finding translators trained in the City’s technical fields. City staff often conduct outreach to the general public as a whole and are limited in their capacity to target specific demographics due to time and financial constraints. Moreover, engaging immigrant residents in meaningful discussions requires facilitation skills that many staff are not trained in. The City of Richmond has a role in supporting immigrant civic engagement and there are opportunities for the City to improve their outreach to immigrant communities. The following table is a summary of this report’s recommendations, based on the feedback from this project’s participants.

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)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,144
Score d'incertitude au seuil0,319

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0430,007
Communication savante0,0080,002
Science ouverte0,0020,009
Intégrité de la recherche0,0030,005
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

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 Opus0,022
Tête enseignante GPT0,219
Écart entre enseignants0,196 · 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'étudeQualitatif
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

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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