2017
DOI: 10.1177/0920203x16689234
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The void in public discourse and the limitations of the equal education campaign in Beijing

Abstract: Because of the huge impact of the hukou system (户口制度) on the allocation of educational resources in China, migrant children’s access to schools has long been circumscribed. Since 2009, a group of migrant parents in Beijing has been involved in a movement demanding their children’s right to sit for the college entrance exam in the city. Using ethnographic methods, this article reviews how the idea of equal education was contested among four groups: (1) liberal intellectuals as the leaders of the movement; (2) m… Show more

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Cited by 4 publications
(3 citation statements)
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References 32 publications
(41 reference statements)
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“…Phrases provide useful semantic information for the IDRR problem. Machine learning methods for IDRR usually rely on lexical features [15,16] (e.g., word pair) or syntactic features [19,20] (e.g., syntactic production rules). These features can be word or phrase level.…”
Section: Phrase Extractionmentioning
confidence: 99%
See 1 more Smart Citation
“…Phrases provide useful semantic information for the IDRR problem. Machine learning methods for IDRR usually rely on lexical features [15,16] (e.g., word pair) or syntactic features [19,20] (e.g., syntactic production rules). These features can be word or phrase level.…”
Section: Phrase Extractionmentioning
confidence: 99%
“…Machine learning-based methods use linguistics features to capture semantic information inside an argument pair. Common linguistic features include lexical features [13,14], contextual features [15,16], syntactic features [17,18], and implicit connectives [19,20]. Such features will then be encoded into numerical vectors and fed into a machine learning classifier to predict the final relation.…”
Section: Introductionmentioning
confidence: 99%
“…Zhao et al [19] proposed a method for online parameter identification using a least squares recursive algorithm, enabling real-time, online identification for the diagnosis of abnormal leakage conditions in heat exchangers. Intelligent parameter identification methods include neural networks [20,21], particle swarm algorithms [22], genetic algorithms (GA) [23], and reinforcement learning (RL) technology. Wang et al [24] utilized a Radial Basis Function (RBF) neural network to establish quantitative relationships between system input variables and model parameters, enabling the identification of unknown parameters and obtaining a relatively accurate model for the heat exchange energy-saving system.…”
Section: Introductionmentioning
confidence: 99%