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Enregistrement W42259626

Urban Education Challenges around the Globe: A Global Education Network of Urban Districts Explores Common Problems with the Hope of Identifying and Sharing Successful Responses

2013· article· en· W42259626 sur OpenAlex

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Notice bibliographique

RevuePhi Delta Kappan · 2013
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSchool Choice and Performance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGlobePoliticsDiversity (politics)Economic growthPopulationPolitical scienceSociologyGeographyPsychologyDemography
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In 2008, for the first time, more than half the world's population lived in cities, hives of economic activity and social diversity. Because these are the places people go seeking opportunity and adventure, they tend to have large migrant populations. Because they are so diverse, cities tend to have more fractious politics. So, cities confront special challenges in trying to provide high-quality public education. What does it mean to provide a good education in places with great disparities in wealth and big differences in values? The Asia Society, a New York-based organization that promotes links between North America and Asia, has created the Global Cities Education Network to see what these urban systems can learn from each other and to assess the extent to which the challenges and potential strategies for urban education transcend borders and cultures. Does Seoul have something to teach Chicago? Can Shanghai benefit from the experience of Toronto? The cities in the network--Chicago, Denver, Hong Kong, Melbourne, New York, Seattle, Seoul, Shanghai, Singapore, and Toronto--and their school systems vary enormously in almost every way. They range in size from under 100,000 students (Denver) to more than a million (Shanghai). Singapore and Toronto have fairly strong central authorities; Denver and Hong Kong are quite decentralized. Seoul and Shanghai are largely unicultural and unilingual (though sometimes with dialects or other kinds of minorities); Chicago and Melbourne are highly diverse. Political systems, cultures, and influences also vary greatly, although every city faces multiple and diverse pressures from its different interest groups. All of the countries involved are experiencing growth in inequalities in wealth and income. Because socioeconomic status remains a powerful influence on school outcomes, these growing gaps make the work of schools more difficult. Indeed, school critics are probably unreasonable in expecting schools to keep reducing achievement gaps when inequality in the society around them is growing. Not surprisingly, the cities have both important commonalities and important differences. For example, all are concerned with gaps in achievement among students and schools--even though the data suggest that these gaps are bigger in the English-speaking countries than in Korea, Singapore, or Hong Kong. Every system is looking for ways to ensure that every school offers a strong education to all students, and every system, including high-performing Singapore, recognizes that it falls short of this aim. Although the belief that every student can benefit from and should get a good education now seems self-evident, this really is quite a new idea and continues to threaten many taken-for-granted aspects of schooling, such as the ability of parents to choose schools in which their children only meet others with similar backgrounds. Leaders in all systems face pressures in this area, in particular from affluent parents whose students are already doing well and want to maintain their advantage through such mechanisms as school choice, unequal provision of resources, private tutoring, or, in some Asian cities, cram schools. Political and educational leaders in urban systems are struggling with how to balance these tensions in ways that are educationally sound and politically acceptable. All of the systems worry about how to build excellence among teachers and other staff. The importance of good teaching and effective leadership are universally recognized. The question is how a system with hundreds or thousands of schools can best support that excellence. How should professional learning be organized? How should leaders be selected, developed, and assigned? How do large systems match skills to needs most effectively? A third concern in these cities is being able to focus attention on what is truly important. Big systems face many pressures every day that distract everyone from the key tasks of teaching and learning. …

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil0,993

Scores Codex et Gemma par catégorie

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

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 Opus0,050
Tête enseignante GPT0,320
Écart entre enseignants0,270 · 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