The development of Canadian student's political conceptions

Two major considerations underlie a set of proposals, outlined in this paper, for a different technology of investigating the psychology of political development in Canadian children and adolescents. First, the paper deals with Hodgetts' (1968) findings, which point out a clear need for further study of the development of political orientations of Canadian students. Second, the paper surveys the typically normative, atheoretical, and methodologically constraining approaches of American investigations of political development in children. Recommendations are advanced to facilitate theory-building and methodological improvement in future investigations. Specific suggestions deal with the construction of an instrument that will elicit scorable information.

---

Résumé Il y a deux considérations principales qui forment la base de propositions présentées dans cet article visant à une technologie différente d'étudier la psychologie du développement politique des enfants et des adolescents canadiens. Premièrement, cet article traite des conclusions de Hodgetts (1968) qui signalent la nécessité d'une étude plus poussée du développement des orientations politiques des étudiants canadiens. Deuxièmement, cet article examine les approches typiquement normatives et nonthéoriques des études américaines du développement politique chez les enfants; cette manière d'aborder le problème est restrictive du point de vue méthodologique. On fait des recommandations afin de faciliter la construction des théories et le progrès méthodologique dans les études à venir. Des suggestions spécifiques touchent à la construction d'un instrument qui obtiendra des renseignements qu'on peut noter.

---

---

OISE     

A scaleable document clustering approach for large document corpora

In this paper, the scalability and quality of the contextual document clustering (CDC) approach is demonstrated for large data-sets using the whole Reuters Corpus Volume 1 (RCV1) collection. CDC is a form of distributional clustering, which automatically discovers contexts of narrow scope within a document corpus. These contexts act as attractors for clustering documents that are semantically related to each other. Once clustered, the documents are organized into a minimum spanning tree so that the topical similarity of adjacent documents within this structure can be assessed. The pre-defined categories from three different document category sets are used to assess the quality of CDC in terms of its ability to group and structure semantically related documents given the contexts. Quality is evaluated based on two factors, the category overlap between adjacent documents within a cluster, and how well a representative document categorizes all the other documents within a cluster. As the RCV1 collection was collated in a time ordered fashion, it was possible to assess the stability of clusters formed from documents within one time interval when presented with new unseen documents at subsequent time intervals. We demonstrate that CDC is a powerful and scaleable technique with the ability to create stable clusters of high quality. Additionally, to our knowledge this is the first time that a collection as large as RCV1 has been analyzed in its entirety using a static clustering approach.