The progressive ideals behind open government data are being used to further interests of the neoliberal state.

A range of social actors are pushing for Open Government Data, from open research advocates to the private sector, resulting in a complex and contested landscape. Jo Bates examines recent developments on how the government have been able to use the rhetoric of transparency for political ends, paving the way for the implementation of long term austerity. She argues we cannot make assumptions about the benefits of ‘openness’ and must continue to revisit the data infrastructure and governance framework

Towards a critical data science – the complicated relationship between data and the democratic project.

What is driving the rise in data-driven techniques used by politicians and political campaigns to connect with the concerns and needs of citizens? Will a data-driven approach to political campaign messaging disrupt the “echo chamber” effect that is perceived to emerge within online spaces? Jo Bates finds the role of data science in the development of the democratic process is still far from certain

The Emergence of Libyan Networked Publics: Social Media Use during and after the Libyan Uprising

It is often claimed that social media sites such as Facebook played a key role during the so-called ‘Arab Spring’. Yet there have been few attempts to track what happened during and after the Libyan uprising, and how social media are – and are not - contributing to the development of revolutionary and post-revolutionary public sphere in the Libyan context. In Libya, there was an explosive growth in social media use during the post-uprising period. This rapid growth could be seen to potentially form the basis for the emergence of a new democratic, networked public sphere. By engaging with different conceptualizations and various critiques of Habermas’[1] public sphere concept, this study aims to explore the nature of emergent Libyan digital publics, and their possible role in transforming the Libyan public sphere

Co-observing the weather, co-predicting the climate: Human factors in building infrastructures for crowdsourced data

This paper investigates the embodied performance of 'doing citizen science'. It examines how 'citizen scientists' produce scientifi c data using the resources available to them, and how their socio-Technical practices and emotions impact the construction of a crowdsourced data infrastructure. We found that conducting citizen science is highly emotional and experiential, but these individual experiences and feelings tend to get lost or become invisible when user-contributed data are aggregated and integrated into a big data infrastructure. While new meanings can be extracted from big data sets, the loss of individual emotional and practical elements denotes the loss of data provenance and the marginalisation of individual eff orts, motivations, and local politics, which might lead to disengaged participants, and unsustainable communities of citizen scientists. The challenges of constructing a data infrastructure for crowdsourced data therefore lie in the management of both technical and social issues which are local as well as global

Sounding out maerl sediment thickness : an integrated data approach

This work was supported by the Natural Environment Research Council [Grant Number NE/S007342/1]. This research was also supported by grants from Marine Alliance for Science and Technology for Scotland (MASTS) Biogeochemistry Forum, MASTS Coastal Forum, and Sea-Changers.Maerl beds are listed as a priority marine feature in Scotland. They are noted for creating suitable benthic habitat for diverse communities of fauna and flora and in supporting a wide array of ecosystem services. Within the context of climate change, they are also recognised as a potential blue carbon habitat through sequestration of carbon in living biomass and underlying sediment. There are, however, significant data gaps on the potential of maerl carbon sequestration which impede inclusion in blue carbon policy frameworks. Key data gaps include sediment thickness, from which carbon content is extrapolated. There are additional logistical and financial barriers associated with quantification methods that aim to address these data gaps. This study investigates the use of sub-bottom profiling (SBP) to lessen financial and logistical constraints of maerl bed sediment thickness estimation and regional blue carbon quantification. SBP data were cross validated with cores, other SBP data on blue carbon sediments, and analysed with expert input. Combining SBP data with estimates of habitat health (as % cover) from drop-down video (DDV) data, and regional abiotic data, this study also elucidates links between abiotic and biotic factors in determining maerl habitat health and maerl sediment thickness through pathway analysis in structural equation modelling (SEM). SBP data were proved to be sufficiently robust for identification of maerl sediments when corroborated with core data. SBP and DDV data of maerl bed habitats in Orkney exhibited some positive correlations of sediment thickness with maerl % cover. The average maerl bed sediment thickness was 1.08 m across all ranges of habitat health. SEM analysis revealed maerl bed habitat health was strongly determined by abiotic factors. Maerl habitat health had a separate positive effect on maerl bed sediment thickness.Peer reviewe

Data Journeys as an approach for exploring the socio-cultural shaping of (big) data: the case of climate science in the United Kingdom

The paper reports on a pilot study aimed at developing a data journeys approach for critically exploring the socio-cultural shaping of interconnected data infrastructures, and presents initial findings from The Secret Life of a Weather Datum research project which applies the approach to explore the socio-cultural values and practices interacting with weather and climate data infrastructures. Through drawing on the data journeys concept to guide and inform the selection of sites for data collection, we begin to demonstrate the utility of the approach for beginning to build a picture of the “contingent and contested” (Dalton and Thatcher, 2014) relations between people, interconnected in time and space through data infrastructures, that are core to the development and shaping of climate data and knowledge. We also begin to draw out the interrelations between local and global spaces and infrastructures; and to ground amorphous ‘big’ data infrastructures in local sites and cultures of production.ye

Mapping Data Journeys: Design for an interactive web site

In this poster we present an overview of our approach to researching the information infrastructure for weather and climate data in the UK, which aims to map the various data journeys of an individual datum through this big data environment, and to uncover the socio-cultural values that shape the data and processes involved in data production, transformation, use and reuse. We then illustrate how we will disseminate our findings through the design of a forthcoming interactive web site, which presents the data journeys using a path/map metaphor, enabling the exploration of four interconnected case studies and several cross-cutting themes, in a way that is both flexible for the user and expandable, as the research progresses further.ye

State-steered smartmentality in Chinese smart urbanism

This study explores the socio-political shaping of Chinese smart urbanism by examining the power relations between the government (national and municipal), private firms and citizens embedded in smartmentality. Our exploration begins with teasing out key analytical standpoints of Alberto Vanolo’s concept of smartmentality applied in neoliberal practices of smart urbanism. Through this analytical framework, we conceptualise Chinafied smartmentality and illustrate how it is actually playing out in China by undertaking documentary research and in-depth interviews from an inductive case study of the Smart Transportation System (STS) in the city of Shijiazhuang. We observe that the idea of Chinafication extends smartmentality with a focus on the power dynamic. We further argue that this Chinafied smartmentality implies uncritical technological solutionism that is state-steered in nature and citizen participation in digital platforms that is performed with limited roles and power of being included. The paper concludes by calling for future research on the critical examination of value co-creation for shaping a truly citizen-centric mode of governance in Chinese smart urbanism

Efficient Few-Shot Learning Without Prompts

Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting training (PET), have achieved impressive results in label-scarce settings. However, they are difficult to employ since they are subject to high variability from manually crafted prompts, and typically require billion-parameter language models to achieve high accuracy. To address these shortcomings, we propose SetFit (Sentence Transformer Fine-tuning), an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers (ST). SetFit works by first fine-tuning a pretrained ST on a small number of text pairs, in a contrastive Siamese manner. The resulting model is then used to generate rich text embeddings, which are used to train a classification head. This simple framework requires no prompts or verbalizers, and achieves high accuracy with orders of magnitude less parameters than existing techniques. Our experiments show that SetFit obtains comparable results with PEFT and PET techniques, while being an order of magnitude faster to train. We also show that SetFit can be applied in multilingual settings by simply switching the ST body. Our code is available at https://github.com/huggingface/setfit and our datasets at https://huggingface.co/setfit