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The lessons learned from the Fukushima Daiichi accident have focused on preventive measures designed to protect nuclear reactors, and crisis management plans. Although there is still no end in sight to the accident that occurred on March 11, 2011, how engineers have handled the aftermath offers new insight into the capacity of organizations to adapt in situations that far exceed the scope of safety standards based on probabilistic risk assessment and on the comprehensive identification of disaster scenarios. Ongoing crises in which conventional resources are lacking, but societal expectations are high, call for “engineering thinking in emergency situations.” This is a new concept that emphasizes adaptability and resilience within organizations—such as the ability to create temporary new organizational structures; to quickly switch from a normal state to an innovative mode; and to integrate a social dimension into engineering activities. In the future, nuclear safety oversight authorities should assess the ability of plant operators to create and implement effective engineering strategies on the fly, and should require that operators demonstrate the capability for resilience in the aftermath of an accident.

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The unprecedented scale of disinformation on the Internet for more than a decade represents a serious challenge for democratic societies. When this process is focused on a well-established subject such as climate change, it can subvert measures and policies that various governmental bodies have taken to mitigate the phenomenon. It is therefore essential to effectively identify and counteract fake news on climate change. To do this, our main contribution represents a novel dataset with more than 2300 articles written in French, gathered using web scraping from all types of media dealing with climate change. Manual labeling was performed by two annotators with three classes: “fake”, “biased”, and “true”. Machine Learning models ranging from bag-of-words representations used by an SVM to Transformer-based architectures built on top of CamemBERT were built to automatically classify the articles. Our results, with an F1-score of 84.75% using the BERT-based model at the article level coupled with hand-crafted features specifically tailored for this task, represent a strong baseline. At the same time, we highlight perceptual properties as text sequences (i.e., fake, biased, and irrelevant text fragments) at the sentence level, with a macro F1 of 45.01% and a micro F1 of 78.11%. Based on these results, our proposed method facilitates the identification of fake news, and thus contributes to better education of the public.

Feedback from industrial accidents is provided by various state or even international, institutions, and lessons learned can be controversial. However, there has been little research into organizational learning at the international level. This article helps to fill the gap through an in-depth review of official reports of the Fukushima Daiichi accident published shortly after the event. We present a new method to analyze the arguments contained in these voluminous documents. Taking an intertextual perspective, the method focuses on the accident narratives, their rationale, and links between "facts," "causes," and "recommendations." The aim is to evaluate how the findings of the various reports are consistent with (or contradict) "institutionalized knowledge," and identify the social representations that underpin them. We find that although the scientific controversy surrounding the results of the various inquiries reflects different ethical perspectives, they are integrated into the same utopian ideal. The involvement of multiple actors in this controversy raises questions about the public construction of epistemic authority, and we highlight the special status given to the International Atomic Energy Agency in this regard.