“We Can’t Just Wrap Ourselves in the Flag:” Labour Nationalism, Global Solidarity, and the 2016 Fight to Save gm Oshawa

Global labour studies scholarship has increasingly recognized the importance of building global solidarity of workers and their unions in response to globalization. Despite this, the labour movement’s embrace of global solidarity as a response to globalization has been incomplete, and at times contradictory. The more common response to globalization has been labour nationalism, which has commanded far less attention in the literature. This paper considers labour nationalism from the perspective of emerging theories of global solidarity, offering a 2016 rank-and-file-driven campaign to save a General Motors plant in Ontario as a case study in labour nationalism. Although nationalism continues to be a relatively effective mobilizing device, Unifor Local 222 has had very little success ‘keeping good jobs in Canada.’ Instead, the union has entrenched a collective action frame that makes space for more xenophobic and racist expressions of nationalism and undermines the prospects of building solidarity abroad and, paradoxically, at home.

A Critical Perspective on Syndemic Theory and Social Justice

Syndemic theory proposes that social phenomena play critical roles in the production and spreading of epidemics and that a syndemic is the result of multiple, adversely interacting epidemics. As currently framed, only the co-occurrence of multiple biological epidemics constitutes a syndemic – social phenomena are treated as risk factors but not epidemics in their own right. I argue that social phenomena such as direct violence (e.g. interpersonal violence, genocide, ethnic cleansing, colonialism, and imperialism) and structural violence (e.g. poverty, racism, historical trauma, and political disenfranchisement) are widespread and adversely affect health in many Indigenous communities, thus meeting the definition of an epidemic. As such, I propose that syndemic theory must be reconceptualized to consider biological and social epidemics, with both types framed as treatable and preventable. Wider acceptance of this frame across disciplines facilitates creation of a collective action frame, which in turn allows us to demand accountability from policymakers – and to demand justice.

Investigating the Impact of Action Framing Manipulations On Ultimatum Game Players

The present study aimed at investigating the impact of action framing manipulations − which consists in priming an individual to act in a certain way − on ultimatum game players. In Experiment 1 (N = 188), participants who played as responders were asked to indicate which offers (from 1€ to 9€) they would either accept or reject. Responders’ minimal offer acceptable was higher in the reject frame than in the accept frame, suggesting that compared to the reject frame the accept action frame primed responders to accept a wider range of offers and favored the acceptance of unfair offers. In Experiment 2 (N = 400), participants played as proposer and were asked to indicate either how much money they wanted to keep for themselves (i.e., selfish frame) or give to the responder (i.e., prosocial frame). Surprisingly, proposers were found to be more generous in the selfish frame than in the prosocial frame. Compared to the prosocial frame, the selfish frame may have led proposers to focus more intensely on the loss inflicted to the responder which triggered a stronger anticipated feeling of guilt, thereby inducing them to be more generous in the selfish than in the prosocial action frame.

Framing in Health Communication: Identifying the Factors That Influence the Re-posting of Social Media Messages during COVID-19 Outbreaks in China (Preprint)

BACKGROUND Social media has become an important tool to implement risk communication in COVID-19 pandemic, and made health information can gain more exposure by re-posting. OBJECTIVE This paper attempts to identify the factors associated with re-posting of social media messages about health information METHODS Content analysis was applied to scrutinize 4396 Weibo posts that were posted by national and provincial public health agencies Weibo accounts and identified features of information sources and information features, and adopted Zero-Inflated Negative Binomial (ZINB) model to analyze the association between these features and the frequency of message being re-posted. RESULTS Results showed that the followers and the governmental level of information sources are correlated with increased message reposting. The information features, such as hashtags#, picture, video, emotional(!), and the usage of severity, reassurance, efficacy and action frame were associated with increased message reposting behaviors, while hyperlink and usage of uncertainty frame correlated with reduced message reposting behaviors. CONCLUSIONS The features of health information sources, structures , style and content should be paid close attention by health organizations and medical professionals to satisfy the public’s information needs and preferences, promote the public's health engagement. Suitable information systems designing, and health communication strategies making during different stages of the pandemic may improve public awareness of the COVID-19, alleviate negative emotions, promote preventive measures to curb the spread of the virus.

COVID health crisis and chronic illness: Protocol for a qualitative study (Preprint)

BACKGROUND The acute nature of COVID-19 epidemic puts a strain on the health resources usually dedicated to chronic illness. Induced changes of care practices and networks had major repercussions on the experience of chronically ill people. This paper presents the research protocol PARCOURS-COVID, and aims to study the effects of such a reorganization on their usual care network which fosters and qualify its quality and continuum. OBJECTIVE Our first objective is to document chronically ill people experience through its transformations and adaptations, both in its practical dimension – daily life and care – and subjective dimension (psychosocial experience of illness and relationship to the health system). The second objective of the study is to identify and reconstruct these reorganizations during the lockdown and the post-lockdown period, in order to grasp their repercussions chronologically and structurally. The last objective is to produce recommendations for adapting the healthcare system to future crises by better acknowledging the experience of chronic patients, their involvement and consultation in the preparation and management of a health crisis. METHODS The PARCOURS-COVID study is a qualitative and participatory research involving patient organizations as research partners and members of patient organizations as part of the research team. Three group of chronic diseases have been selected regarding the specificities of the care network they mobilise: cystic fibrosis and kidney disease, haemophilia and mental disorders. Four consecutive phases will be conducted: i) preparatory interviews; ii) in-depth individual interviews with patients of each pathology will be analysed using a qualitative method of thematic analysis; iii) results of these both latter will be triangulated through interviews with members of each patient’s care ecosystem; iv) focus-groups will be organized to discuss the results with research participants ie. representatives of chronic disease associations, patients, actors of the medical, psycho-social and family care network in a research-action frame. RESULTS The protocol study has undergone a peer-review by the French National Research Agency's scientific committee and has been approved by the Research Ethical Committee of the Université de Paris (registration number: IRB 00012020-59 June 28th, 2020). The project was funded from July 2020 through March 2021. Expected results will be disseminated in 2021 and 2022. CONCLUSIONS Our findings will better inform the stakes of the current health crisis on the management of the chronically ill and, more broadly, any future crisis for a population deemed to be at risk. They will improve health democracy by supporting a better transferability of knowledge between the scientific and citizen communities.

Realistic Speech-Driven Talking Video Generation with Personalized Pose

In this work, we propose a method to transform a speaker’s speech information into a target character’s talking video; the method could make the mouth shape synchronization, expression, and body posture more realistic in the synthesized speaker video. This is a challenging task because changes of mouth shape and posture are coupled with audio semantic information. The model training is difficult to converge, and the model effect is unstable in complex scenes. Existing speech-driven speaker methods cannot solve this problem well. The method proposed in this paper first generates the sequence of key points of the speaker’s face and body postures from the audio signal in real time and then visualizes these key points as a series of two-dimensional skeleton images. Subsequently, we generate the final real speaker video through the video generation network. We take a random sampling of audio clips, encode audio contents and temporal correlations using a more effective network structure, and optimize and iterate network outputs using differential loss and attitude perception loss, so as to obtain a smoother pose key-point sequence and better performance. In addition, by inserting a specified action frame into the synthesized human pose sequence window, action poses of the synthesized speaker are enriched, making the synthesis effect more realistic and natural. Then, the final speaker video is generated by the obtained gesture key points through the video generation network. In order to generate realistic and high-resolution pose detail videos, we insert a local attention mechanism into the key point network of the generated pose sequence and give higher attention to the local details of the characters through spatial weight masks. In order to verify the effectiveness of the proposed method, we used the objective evaluation index NME and user subjective evaluation methods, respectively. Experiment results showed that our method could vividly use audio contentsto generate corresponding speaker videos, and its lip-matching accuracy and expression postures are better than those of previous work. Compared with existing methods in the NME index and user subjective evaluation, our method showed better results.

Pattern recognition of head movement based on mechanomyography and its application

The first part of this study investigated pattern recognition of head movements based on mechanomyography (MMG) signals. Four channel MMG signals were collected from the sternocleidomastoid (SCM) muscles and the splenius capitis (SPL) muscles in the subjects’ neck when they bowed the head, raised the head, side-bent to left, side-bent to right, turned to left and turned to right. The MMG signals were then filtered, normalized and divided using an unequal length segmentation algorithm into a single action frame. After extracting the energy features of the wavelet packet coefficients and the feature of the principal diagonal slices of the bispectrum, the dimension of the energy features were reduced by the Fisher linear discriminant analysis (FLDA). Finally, all the features were classified through the support vector machine (SVM) classifier. The recognition rate was up to 95.92%. On this basis, the second part of this study used the head movements to control a car model for simulating the control of a wheelchair, and the success rate was 85.74%.

Reframing Air Pollution as a Public Health Crisis in California’s San Joaquin Valley

This article examines how clean air advocates in the San Joaquin Valley amplified a collective action frame of air pollution as a public health crisis. This frame serves to mobilize communities and attribute responsibility to relevant authorities while countering the San Joaquin Valley Air District’s narrative that places blame on the public and unalterable aspects of the physical environment, characterizing the problem as largely unchangeable. Core questions are: How did advocates facilitate frame construction and sustain resonance across time and place? How did this frame compel urgent action, including representational change in the Valley Air District Governing Board through the adoption of Senate Bill 719? Findings show that advocates counter the San Joaquin Valley Air District’s frame by shifting the focus on the negative impacts to public health and quality of life, particularly in disproportionately impacted environmental justice communities. Building and leveraging a resonant frame required strategic “stretch” in capacity while cultivating collective identity and solidarity. Within the Valley Air District, the change in governing board composition has measurably impacted dialog and provided a more welcoming reception of public participation at meetings, with the doctor and scientist appointees providing critical perspectives and contributing to subtle shifts in the agency’s dominant messaging and decision making. While a public health frame has taken shape around air pollution in the San Joaquin Valley, these efforts are not complete or without ongoing strain.

Learning Transferable Self-Attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision

Action recognition in videos has attracted a lot of attention in the past decade. In order to learn robust models, previous methods usually assume videos are trimmed as short sequences and require ground-truth annotations of each video frame/sequence, which is quite costly and time-consuming. In this paper, given only video-level annotations, we propose a novel weakly supervised framework to simultaneously locate action frames as well as recognize actions in untrimmed videos. Our proposed framework consists of two major components. First, for action frame localization, we take advantage of the self-attention mechanism to weight each frame, such that the influence of background frames can be effectively eliminated. Second, considering that there are trimmed videos publicly available and also they contain useful information to leverage, we present an additional module to transfer the knowledge from trimmed videos for improving the classification performance in untrimmed ones. Extensive experiments are conducted on two benchmark datasets (i.e., THUMOS14 and ActivityNet1.3), and experimental results clearly corroborate the efficacy of our method.