Riding out the risks: an ethnographic study of risk perceptions in a South Louisiana bayou community

This ethnographic study explores the risk perceptions of a small unincorporated coastal community in southeastern Louisiana. This community has experienced social and environmental change due to events including tropical storms and hurricanes, erosion, subsidence, oil and gas activities, development, and the impact of global seafood markets. Many global risk perception studies have focused on the perception of risk to human health and property connected with natural and technological disasters, but few have explored the issue of minorities and small at-risk communities. To explore this theoretical and methodological gap, this study uses a variety of qualitative ethnographic methods to examine a small at-risk community of minorities. The central question of this research asks: Why does a marginalized community with few resources choose to stay in an area that they perceive to be burdened with environmental and social threats? Findings suggest that geographical displacement is a greater ‘risk’ than living in an area burdened with continual environmental and social threats. As Meda states: “…if we follow the same traditional ways of evacuating for a storm that our fathers and grandfathers did, we pack up and go to our boats. Traditionally that’s what we do, that’s what we know, that’s how we keep ourselves safe. But the land has changed…the land standing between us and the storms has diminished because of erosion, subsidence, and all of these other things that came into play. Now when storms come, we get flooded with greater frequency and with higher tides and the porosity of the currents that come through, its stronger and stronger…so, those safe harbors will no longer be safe harbors and our traditional ways of evacuating, we will have to find somewhere else to go. Because they will no longer be able to sustain us and its something that we know and its something that we are going to have to face, but because of who we are and because of…our ties to the community…life at all costs is better than anything that I can think of. But we do stay and we fight for what we have and risk is part of it.

Automatic recognition of multiparty human interactions using dynamic Bayesian networks

Relating statistical machine learning approaches to the automatic analysis of multiparty communicative events, such as meetings, is an ambitious research area. We have investigated automatic meeting segmentation both in terms of “Meeting Actions” and “Dialogue Acts”. Dialogue acts model the discourse structure at a fine grained level highlighting individual speaker intentions. Group meeting actions describe the same process at a coarse level, highlighting interactions between different meeting participants and showing overall group intentions. A framework based on probabilistic graphical models such as dynamic Bayesian networks (DBNs) has been investigated for both tasks. Our first set of experiments is concerned with the segmentation and structuring of meetings (recorded using multiple cameras and microphones) into sequences of group meeting actions such as monologue, discussion and presentation. We outline four families of multimodal features based on speaker turns, lexical transcription, prosody, and visual motion that are extracted from the raw audio and video recordings. We relate these lowlevel multimodal features to complex group behaviours proposing a multistreammodelling framework based on dynamic Bayesian networks. Later experiments are concerned with the automatic recognition of Dialogue Acts (DAs) in multiparty conversational speech. We present a joint generative approach based on a switching DBN for DA recognition in which segmentation and classification of DAs are carried out in parallel. This approach models a set of features, related to lexical content and prosody, and incorporates a weighted interpolated factored language model. In conjunction with this joint generative model, we have also investigated the use of a discriminative approach, based on conditional random fields, to perform a reclassification of the segmented DAs. The DBN based approach yielded significant improvements when applied both to the meeting action and the dialogue act recognition task. On both tasks, the DBN framework provided an effective factorisation of the state-space and a flexible infrastructure able to integrate a heterogeneous set of resources such as continuous and discrete multimodal features, and statistical language models. Although our experiments have been principally targeted on multiparty meetings; features, models, and methodologies developed in this thesis can be employed for a wide range of applications. Moreover both group meeting actions and DAs offer valuable insights about the current conversational context providing valuable cues and features for several related research areas such as speaker addressing and focus of attention modelling, automatic speech recognition and understanding, topic and decision detection

Factors That Influence the Decision-Making of an Integrated Rehabilitation Team When Choosing a Post-Hospital Discharge Destination For Survivors of Stroke

Stroke is one of the more disabling conditions which may result in the inability for survivors to care for themselves independently. Stroke survivors benefit most when they receive early onset assessment, treatment, and rehabilitation. Increasingly, stroke care in Canadian hospitals relies on an interdisciplinary rehabilitation team approach to provide immediate rehabilitation services and to make decisions about discharge destination for stroke survivors. Currently, there is little research on how interdisciplinary rehabilitation teams decide upon rehabilitation placements for stroke survivors or how individuals on the team, stroke survivors, or their families participate in and contribute to this decision. This research studied the culture of the interdisciplinary rehabilitation team to understand the specific client, clinical, and family situations considered by team members and how that information was communicated and evaluated by them during their decision-making. To address the research question, the researcher undertook an ethnographic study of a health care team on a stroke unit of a Canadian hospital. Based on observations of the interdisciplinary rehabilitation team and interviews with team members, the study found that decisions about post-hospital discharge destination were conditioned by variables related to the social, economic, and policy context; interactions among members of the team; and the condition of stroke survivors or their families and their ability and willingness to contribute to home care

A qualitative approach to examining the rules of a community college and industry partnership.

The purpose of this study was to qualitatively examine the governance structure of a successful community college and industry advisory board that collaborate to improve regional workforce development initiatives. The institutional analysis and development (IAD) framework was used as a lens to describe the partnership. The results determined that there were many informal rules that governed the relationship between the community college and industry partners, which led to successful implementation of decisions. The community college leaders created the informal rules with the purpose of encouraging involvement among industry stakeholders, sharing power among all the participants, and facilitating communication. The findings are consistent with the literature in collaboration. Frequent and open communication, outcomes that benefit all stakeholders, and other positive institutional designs aid in the success of a community college and industry partnership. Keywords: community college, collaboration, industry, partnerships, industry advisory board, governance, institutional analysis and development framework, rules, cooperation, regional workforce development

Pragmatics and the consequentiality of talk: a study of members' methods at a planning application meeting

This study explores how talk is consequential by examining the sequential and pragmatic phenomena in talk-in-interaction. Reflecting the work of conversation analysis (CA), the approach assumes that the consequentiality of a 'context' must be demonstrated by the informants' sequential practices (cf. Schegloff 1987, Boden and Zimmerman 1991). However, in this study a model of consequentiality is proposed, in which not only sequential phenomena but also pragmatic categories are included within the repertoire of members' methods. In this way, the indexicality of language as explained by pragmatic theory is seen to contribute to the account of talk as consequential. The data represent a meeting between an urban planning department and a national development company in which a planning application is discussed. As such, members' methods are seen to invoke the institutional nature of the encounter, in which the formality of the setting and the work-related membership of the interactants is systematically oriented to. The talk consists of a series of negotiated issues in which the developers and the planners propose different candidate outcomes reflecting each party’s professional aims and the constraints they consider themselves to operate under. In particular, the analysis shows that candidate outcomes are largely managed by sequential preference systems and pragmatically characterized face-address (Brown and Levinson 1978, 1987).The notion of reflexivity is also seen as a significant component in the study of consequentiality. While the concept is a basic assumption in a CA framework (Garfinkel and Sacks 1969) and is also recognized as fundamental in pragmatic inquiry (Lucy 1993), few studies provide a detailed analysis of members' reflexive awareness of the contexts they create. In this study, the interactants' metalinguistic and metapragmatic orientation, invoked by both pragmatic and sequential methods, is shown to be a prevalent members' resource for indicating awareness of consequentiality. Finally, observations of the kind made in this thesis, wherein pragmatic categories both work together and are systematically related to the sequential environment, contribute to a general re-analysis of pragmatic meaning. At the same time, the interaction of pragmatic and sequential features also represents a dynamic starting point for developing new methodological categories for investigating talk-in-interaction

Robust subspace learning for static and dynamic affect and behaviour modelling

Machine analysis of human affect and behavior in naturalistic contexts has witnessed a growing attention in the last decade from various disciplines ranging from social and cognitive sciences to machine learning and computer vision. Endowing machines with the ability to seamlessly detect, analyze, model, predict as well as simulate and synthesize manifestations of internal emotional and behavioral states in real-world data is deemed essential for the deployment of next-generation, emotionally- and socially-competent human-centered interfaces. In this thesis, we are primarily motivated by the problem of modeling, recognizing and predicting spontaneous expressions of non-verbal human affect and behavior manifested through either low-level facial attributes in static images or high-level semantic events in image sequences. Both visual data and annotations of naturalistic affect and behavior naturally contain noisy measurements of unbounded magnitude at random locations, commonly referred to as ‘outliers’. We present here machine learning methods that are robust to such gross, sparse noise. First, we deal with static analysis of face images, viewing the latter as a superposition of mutually-incoherent, low-complexity components corresponding to facial attributes, such as facial identity, expressions and activation of atomic facial muscle actions. We develop a robust, discriminant dictionary learning framework to extract these components from grossly corrupted training data and combine it with sparse representation to recognize the associated attributes. We demonstrate that our framework can jointly address interrelated classification tasks such as face and facial expression recognition. Inspired by the well-documented importance of the temporal aspect in perceiving affect and behavior, we direct the bulk of our research efforts into continuous-time modeling of dimensional affect and social behavior. Having identified a gap in the literature which is the lack of data containing annotations of social attitudes in continuous time and scale, we first curate a new audio-visual database of multi-party conversations from political debates annotated frame-by-frame in terms of real-valued conflict intensity and use it to conduct the first study on continuous-time conflict intensity estimation. Our experimental findings corroborate previous evidence indicating the inability of existing classifiers in capturing the hidden temporal structures of affective and behavioral displays. We present here a novel dynamic behavior analysis framework which models temporal dynamics in an explicit way, based on the natural assumption that continuous- time annotations of smoothly-varying affect or behavior can be viewed as outputs of a low-complexity linear dynamical system when behavioral cues (features) act as system inputs. A novel robust structured rank minimization framework is proposed to estimate the system parameters in the presence of gross corruptions and partially missing data. Experiments on prediction of dimensional conflict and affect as well as multi-object tracking from detection validate the effectiveness of our predictive framework and demonstrate that for the first time that complex human behavior and affect can be learned and predicted based on small training sets of person(s)-specific observations.Open Acces

Rethinking English for academic purposes : towards a performance-centred pedagogy

Contains fulltext : mmubn000001_027906248.pdf (publisher's version ) (Open Access)Promotor : H. Bloemendal125 p