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Record 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 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePhi Delta Kappan · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsGlobePoliticsDiversity (politics)Economic growthPopulationPolitical scienceSociologyGeographyPsychologyDemography
DOInot available

Abstract

fetched live from 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. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.320
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it