When Is the Teacher? Reflections on Life Writing, Social Fiction, and Film

In this paper, seven Canadian curriculum researchers investigate and discuss life writing as a mode of educational inquiry and curricular theorizing through which educators can attend to the tensions and complexities of teaching and learning in a variety of curricular and pedagogical contexts. Drawing from their individual and collective research in creative methods of arts-based inquiry, they explore how life writing, with its multiple modalities between creative nonfiction, fiction, poetry, theatre arts, fine arts, and multimedia, can open up possibilities for researchers, teachers, and students to rethink and re-enact education as an inspiriting, heart-full, and empathetic endeavour

Document Filtering for Long-tail Entities

Filtering relevant documents with respect to entities is an essential task in the context of knowledge base construction and maintenance. It entails processing a time-ordered stream of documents that might be relevant to an entity in order to select only those that contain vital information. State-of-the-art approaches to document filtering for popular entities are entity-dependent: they rely on and are also trained on the specifics of differentiating features for each specific entity. Moreover, these approaches tend to use so-called extrinsic information such as Wikipedia page views and related entities which is typically only available only for popular head entities. Entity-dependent approaches based on such signals are therefore ill-suited as filtering methods for long-tail entities. In this paper we propose a document filtering method for long-tail entities that is entity-independent and thus also generalizes to unseen or rarely seen entities. It is based on intrinsic features, i.e., features that are derived from the documents in which the entities are mentioned. We propose a set of features that capture informativeness, entity-saliency, and timeliness. In particular, we introduce features based on entity aspect similarities, relation patterns, and temporal expressions and combine these with standard features for document filtering. Experiments following the TREC KBA 2014 setup on a publicly available dataset show that our model is able to improve the filtering performance for long-tail entities over several baselines. Results of applying the model to unseen entities are promising, indicating that the model is able to learn the general characteristics of a vital document. The overall performance across all entities---i.e., not just long-tail entities---improves upon the state-of-the-art without depending on any entity-specific training data.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on Information and Knowledge Management. 201

Language, identity, and relations : We gaze as visual-literacy and arts-based inquiry in teaching

The project discusses and shares the creation of a social fiction, entitled We Gaze, which presents themes and images significant to curriculum and pedagogy surrounding and connecting literacies, identity, and relationship from the point of view of an observer of daily life, a teacher, and a participant in society. This arts-based inquiry is situated between narrative and relational knowing (Clandinin, Murphy, Huber, & Orr, 2010; Knowles & Cole, 2008) researching from within the stories and phenomenon being studied. The multi-modal text interposes a non-verbal vocabulary (Rahn, 2007); addresses identity being tied to missed and mixed messages of words; and amplifies the individual stories and relationships of an ordinary life in the struggle to connect engrossing and separate lifeworlds (van Manen, 1990). Drawing from arts-based and multi-literacies research in education, phenomenological and hermeneutic philosophy, literary and fine arts, some of the truths of being human are explored. These encompass disclosure, honesty, identity, privacy, relatedness, creativity, as well as the life of the text. Included throughout are pedagogical implications of observing and creating artfully. Attending thoughtfully to one’s stories and the stories of others, as revealed in verbal and non-verbal literacies, is critical to fully becoming human. Found within the narratives, images, and poetry, as well as in the intertextual spaces (Sumara, 1995), the inquiry reveals how the vulnerability implicit in sharing text allows for authentic relation with the world

LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation

Successfully training a deep neural network demands a huge corpus of labeled data. However, each label only provides limited information to learn from and collecting the requisite number of labels involves massive human effort. In this work, we introduce LEAN-LIFE, a web-based, Label-Efficient AnnotatioN framework for sequence labeling and classification tasks, with an easy-to-use UI that not only allows an annotator to provide the needed labels for a task, but also enables LearnIng From Explanations for each labeling decision. Such explanations enable us to generate useful additional labeled data from unlabeled instances, bolstering the pool of available training data. On three popular NLP tasks (named entity recognition, relation extraction, sentiment analysis), we find that using this enhanced supervision allows our models to surpass competitive baseline F1 scores by more than 5-10 percentage points, while using 2X times fewer labeled instances. Our framework is the first to utilize this enhanced supervision technique and does so for three important tasks -- thus providing improved annotation recommendations to users and an ability to build datasets of (data, label, explanation) triples instead of the regular (data, label) pair.Comment: Accepted to the ACL 2020 (demo). The first two authors contributed equally. Project page: http://inklab.usc.edu/leanlife

AutoTriggER: Named Entity Recognition with Auxiliary Trigger Extraction

Deep neural models for low-resource named entity recognition (NER) have shown impressive results by leveraging distant super-vision or other meta-level information (e.g. explanation). However, the costs of acquiring such additional information are generally prohibitive, especially in domains where existing resources (e.g. databases to be used for distant supervision) may not exist. In this paper, we present a novel two-stage framework (AutoTriggER) to improve NER performance by automatically generating and leveraging "entity triggers" which are essentially human-readable clues in the text that can help guide the model to make better decisions. Thus, the framework is able to both create and leverage auxiliary supervision by itself. Through experiments on three well-studied NER datasets, we show that our automatically extracted triggers are well-matched to human triggers, and AutoTriggER improves performance over a RoBERTa-CRFarchitecture by nearly 0.5 F1 points on average and much more in a low resource setting.Comment: 10 pages, 12 figures, Best paper at TrustNLP@NAACL 2021 and presented at WeaSuL@ICLR 202

All Quiet on the Protest Scene? Repertoires of Contention and Protest Actors During the Great Recession

The choice of specific action repertoires allows protesters to increase their visibility and eventually their success. A rise in protest, i.e. a protest wave, often comes with a qualitative expansion of the conflict, which can take two forms: changes in the action repertoire and a growing diversity of involved actors. In this chapter, we examine the types of protest and the types of actors over time. In so doing, we ask whether and how the Great Recession transformed customary action repertoires in southern, north-western, and eastern Europe. Hence, we show variations in the use of commonplace action forms, i.e. demonstrations, strikes, and confrontational and violent actions. We find that demonstrations and strikes remain the dominant form of protest across regions and time periods, while transformations in the action repertoire of contention, in the form of violent events, took place only in some parts of the south and were short lived. Lastly, we turn to actors and we show that protest events increasingly feature social groups without formal organizational structures. We conclude by arguing that contention repertoires remained largely unaffected by the Great Recession; demonstrations were and remained the prevailing form of protest in all three regions during the whole period under study

Are Political Parties Recapturing the Streets of Europe?

Political mobilization in the electoral and protest arenas have long been studied as separate phenomena, following their own, independent dynamic. Parties and protests are rarely examined within the same framework, although the protest engagement of political parties is often assumed to be one of the main driving forces of the wave of protest in southern European countries, those most exposed to the economic crisis. The chapter provides the first large-scale study of protests sponsored by political parties across Europe before and after the Great Recession. It relies on a novel protest event dataset, collected by semi-automated content analysis of news agencies. The data cover protests in thirty countries, from 2000 to 2015. The results show the ‘crowding out’ of political parties as the driving force of the protest wave in southern Europe. We find the highest share of party sponsored protest in eastern Europe, where unlike in north-western and southern Europe, right-wing and non-mainstream parties are also active in protest. In line with the overall findings of the book, our results confirm the distinctive dynamic of protest in the three European macro-regions and put the link between social movements and the new challenger parties in perspective