Notice bibliographique
Résumé
In the past decades, diversity has become a popular catchphrase in theoretical, policy and public discourses in Canadian cities. Toronto is Canada’s most diverse city, wherein a long-standing immigration history coupled by the introduction of the Canadian Multiculturalism policy in the 1970s have rendered diversity a prominent value for the city’s inhabitants (Ahmadi and Tasan-Kok, 2014). Celebration of diversity has become a popular theme in Toronto’s policy and image making, such that many policy documents have proclaimed diversity as the city’s biggest strength. However, while the celebration of diversity has attracted funds and services to inner city Toronto, stereotyping based on different categories of diversity (particularly ethnicity and class) has resulted in the stigmatization and criminalization of poor racialised neighbourhoods located at the edges of the city. Diversity in urban areas may derive from multiple factors such as behaviour, lifestyles, activities, ethnicity, age, gender and sexuality profiles, entitlements and restrictions of rights, labour market experiences, and patterns of spatial distribution. Research on diversity in the past decades has resulted in the creation of an extensive body of work on the notion. However, there are a few gaps in theory which the present study seeks to address, namely: (a) Research on diversity often overlooks the complexity and dynamic nature of diversity and maintains an overemphasis on ethnicity. (b) Despite plentiful evidence for the diversification of peripheral neighbourhoods, the available body of research focuses primarily on inner-city areas, leaving out the more remote rural and suburban areas (Humphris, 2014). (c) There is a tendency to present a ‘flat’ or ‘horizontal’ type of differentiation of diversity, which does not account for the various positions and hierarchies within and between different categories of difference. In light of these gaps, this study seeks to add to our understanding of urban diversity, as perceived and experienced by those who inhabit, frequent and govern urban areas. It answers the following primary research question: How is diversity experienced at the neighbourhood level, as (a) discourse, (b) social reality, and (c) practice? Diversity as discourse refers to the public narratives around diversity, while diversity as social reality concerns the descriptive characteristics that render an area diverse. Diversity as practice refers to policies, programs and local practices that aim towards managing diversity (see also Berg and Sigona, 2013). The research question is investigated in four interconnected chapters, which engage with the three formerly mentioned dimensions to various degrees. The study further makes use of a variety of qualitative and participatory techniques (i.e. qualitative interviews, roundtable talks, participant observations, and focus groups) to gather rigorous empirical data on living with and managing diversity in an inner-suburban neighbourhood of Toronto, namely Jane-Finch.
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 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 | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,053 | 0,027 |
| Communication savante | 0,010 | 0,006 |
| Science ouverte | 0,002 | 0,015 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,001 |
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.
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 ».