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Knowledge of how social movements move, diffuse, and expand collective action events is central to movement scholarship and activist practice. Our purpose is to extend sociological knowledge about how movements (sometimes) diffuse and amplify insurgent actions, that is, how movements move. We extend movement diffusion theory by drawing a conceptual analogue with military theory and practice applied to the case of the organized and highly disciplined nonviolent Nashville civil rights movement in the late 1950s and early 1960s. We emphasize emplacement in a base-mission extension model whereby a movement base is built in a community establishing a social movement school for inculcating discipline and performative training in cadre who engage in insurgent operations extended from that base to outlying events and campaigns. Our data are drawn from secondary sources and semi-structured interviews conducted with participants of the Nashville civil rights movement. The analytic strategy employs a variant of the “extended case method,” where extension is constituted by movement agents following paths from base to outlying campaigns or events. Evidence shows that the Nashville movement established an exemplary local movement base that led to important changes in that city but also spawned traveling movement cadre who moved movement actions in an extensive series of pathways linking the Nashville base to events and campaigns across the southern theater of the civil rights movement. We conclude with theoretical and practical implications.

In contrast to traditional communication systems, slower data rate has always remained a weak link for non-traditional random communication systems (RCSs), which use alpha-stable (a-stable) noise as a carrier. This paper aims to introduce a fast receiver for skewed a-stable noise shift keying (SkaSNSK)-based RCSs.

The introduced receiver is based on the sign of slant estimator (SoSE), which provides rapid estimation of the skewed a-stable random noise signals (RNSs) received from the additive white Gaussian noise channel. The SoSE-based receiver minimizes the number of samples required to extract the encoded information from the received RNSs. This is achieved by manipulating the antipodal properties of the slant/skewness parameter of the a-stable carrier. Hence, a high data rate with relatively low complexity is guaranteed.

In comparison with the previously introduced sinc, logarithmic and modified extreme value method-based receivers, the proposed SoSE-based receiver also achieves improved bit error rate (BER) along with the better covertness values so that the essence of security provided by SkaSNSK-based RCSs remains intact.

Because of the selected range of the associated parameters of the a-stable noise as a carrier, the BER vs MSNR results are may lack applicability for the complete range of values. Therefore, further research is required to produce results in different ranges.

The study includes implications for the hardware development based on the proposed communication scheme.

It can be seen that the paper fulfils the desired need of a fast receiver design for RCS.

Technologically-enhanced language education has shifted from computer-assisted language learning (CALL) to mobile-assisted language learning (MALL), including the use of conversational digital agents, and more recently, towards the use of generative artificial intelligence (AI) large language model (LLM) programmes for language learning purposes. This paper aims to explore the interplay between such posthuman communication and posthumanist applied linguistics, and between digital agents and human agency in response to the increasing permeation of AI in life and learning.

A core team of four researchers investigated how digital agents could be leveraged to support immersive target language learning and practice, focusing specifically on the conversational AI that pervaded digitally-mediated communication prior to the release of generative AI. Each researcher engaged in a digital autoethnography using conversational agents found in the digital wilds to learn a target second language via digital immersion.

Through qualitative data analysis of autoethnographic narratives using NVIVO, four key thematic codes characterizing the learning journeys emerged: context, language learning, posthuman engagement and technological parameters. The posthuman learning experiences conflicted with the multisensory, embodied and embedded ethos of posthumanist applied linguistics, indicating that informed human pedagogical agency must crucially be exercised to benefit from the learning potential of posthuman agents. Interactions with conversational agents did provide small-scale, just-in-time learning opportunities, but these fell short of immersive learning.

The methodology and findings offer a unique and valuable lens on the language learning potential of emerging LLM-based generative agents that are rapidly infusing conversational practices.