Zoekresultaten

Internet on the outstation

Internet on the Outstation provides a new take on the digital divide. Why do whole communities choose to go without the internet when the infrastructure for access is in place? Through an in-depth exploration of the digital practices occurring in Aboriginal households in remote central Australia, the authors address both the dynamics of internet adoption and the benefits that flow from its use. The book challenges us to think beyond the standard explanations for the digital divide, arguing that digital exclusion is not just another symptom of social exclusion. At its heart, Internet on the Outstation is a compelling examination of equality and difference in the digital age, asking: Can internet access help resolve the disadvantages associated with remote living?Internet on the Outstation is the result of a multi-year research collaboration, which included a trial of internet infrastructure, training and maintenance in three small Aboriginal communities (known as outstations). During the research phase, Ellie Rennie, Eleanor Hogan and Julian Thomas were based at the Swinburne Institute for Social Research in Melbourne. Robin Gregory and Andrew Crouch worked at the Centre for Appropriate Technology, an Indigenous-owned research and training organization in Alice Springs. Alyson Wright worked for the Central Land Council, the representative body for traditional owners of the central Australia region.

Reference points

We need mental and physical reference points. We need physical reference points such as signposts to show us which way to go, for example to the airport or the hospital, and we need reference points to show us where we are. Why? If you don’t know where you are, it’s quite a difficult job to find your way, thus landmarks and “lieux de memoire” play an important role in our lives.

Transitie van mbo naar hbo

A deeper dig into Global Learning

International education is a relatively new field and until recently, there was no formal education to prepare practitioners. This means that people working in international education are a colourful and diverse group, coming from a wide range of disciplines, which definitely adds to the attraction of the field. I call international education a field rather than a discipline since it is composed of a variety of established disciplines, such as languages, educational sciences, psychology, business, anthropology, history and even, in my case, classical archaeology. For this lecture, I have chosen to return to my original discipline and discuss global learning as the stages of an archaeological excavation. Cutting though the subsequent layers represents a history of international education but also my own professional history. By digging deeper down, layer after layer, I hope to uncover the essence of global learning in order to make its benefits available for all our students. This lecture consists of four sections. In the first section, I want to go back to the time when archaeology was a new discipline and see what we can learn from the research conducted at that time. In the second section I will reveal the layers of internationalisation and global learning until we come to the layer that we are currently exploring. In the third section, I will look at some of the factors and trends that will have an impact on global learning in the years to come. This shows that circumstances are quite different from when the excavation started and that global education is therefore dynamic. Finally, I will discuss what research the Research Group Global Learning will conduct, how and with whom, in the coming years.

Strategic competences for concrete action towards sustainability: : An oxymoron?

In the current discourses on sustainable development, one can discern two main intellectual cultures: an analytic one focusing on measuring problems and prioritizing measures, (Life Cycle Analysis (LCA), Mass Flow Analysis (MFA), etc.) and; a policy/management one, focusing on long term change, change incentives, and stakeholder management (Transitions/niches, Environmental economy, Cleaner production). These cultures do not often interact and interactions are often negative. However, both cultures are required to work towards sustainability solutions: problems should be thoroughly identified and quantified, options for large change should be guideposts for action, and incentives should be created, stakeholders should be enabled to participate and their values and interests should be included in the change process. The paper deals especially with engineering education. Successful technological change processes should be supported by engineers who have acquired strategic competences. An important barrier towards training academics with these competences is the strong disciplinarism of higher education. Raising engineering students in strong disciplinary paradigms is probably responsible for their diminishing public engagement over the course of their studies. Strategic competences are crucial to keep students engaged and train them to implement long term sustainable solutions.

Coding, prompting, “deprompting”

Prompt design can be understood similarly to query design, as a prompt aiming to understand cultural dimensions in visual research, forcing the AI to make sense of ambiguity as a way to understand its training dataset and biases ( Niederer, S. and Colombo, G., ‘Visual Methods for Digital Research’). It moves away from prompting engineering and efforts to make “code-like” prompts that suppress ambiguity and prevent the AI from bringing biases to the surface. Our idea is to keep the ambiguity present in the image descriptions like in natural language and let it flow through different stages (degrees) of the broken telephone dynamics. This way we have less control over the result or selection of the ideal result and more questions about the dynamics implicit in the biases present in the results obtained.Different from textual or mathematical results, in which prompt chains or asking the AI to explain how it got the result might be enough, images and visual methods assisted by AI demand new methods to deal with that. Exploring and developing a new approach for it is the main goal of this research project, particularly interested in possible biases and unexplored patterns in AI’s image affordances.How could we detect small biases in describing images and creating based on descriptions when it comes to AI? What exactly do the words written by AI when describing an image stand for? When detecting a ‘human’ or ‘science’, for example, what elements or archetypes are invisible between prompting, and the image created or described?Turning an AI’s image description into a new image could help us to have a glimpse behind the scenes. In the broken telephone game, small misperceptions between telling and hearing, coding and decoding, produce big divergences in the final result - and the cultural factors in between have been largely studied. To amplify and understand possible biases, we could check how this new image would be described by AI, starting a broken telephone cycle. This process could shed light not just into the gap between AI image description and its capacity to reconstruct images using this description as part of prompts, but also illuminate biases and patterns in AI image description and creation based on description.It is in line with previous projects on image clustering and image prompt analysis (see reference links), and questions such as identification of AI image biases, cross AI models analysis, reverse engineering through prompts, image clustering, and analysis of large datasets of images from online image and video-based platforms.The experiment becomes even more relevant in light of the results from recent studies (Shumailov et al., 2024) that show that AI models trained on AI generated data will eventually collapse.To frame this analysis, the proposal from Munn, Magee and Arora (2023) titled Unmaking AI Imagemaking introduces three methodological approaches for investigating AI image models: Unmaking the ecosystem, Unmaking the data and Unmaking the outputs.First, the idea of ecosystem was taken for these authors to describe socio-technical implications that surround the AI models: the place where they have been developed; the owners, partners, or supporters; and their interests, goals, and impositions. “Research has already identified how these image models internalize toxic stereotypes (Birnhane 2021) and reproduce forms of gendered and ethnic bias (Luccioni 2023), to name just two issues” (Munn et al., 2023, p. 2).There are also differences between the different models that currently dominate the market. Although Stable Diffusion seems to be the most open due to its origin, when working with images with this model, biases appear even more quickly than in other models. “In this framing, Stable Diffusion becomes an internet-based tool, which can be used and abused by “the people,” rather than a corporate product, where responsibility is clear, quality must be ensured, and toxicity must be mitigated” (Munn et al., 2023, p. 5).To unmaking the data, it is important to ask ourselves about the source and interests for the extraction of the data used. According to the description of their project “Creating an Ad Library Political Observatory”, “This project aims to explore diverse approaches to analyze and visualize the data from Meta’s ad library, which includes Instagram, Facebook, and other Meta products, using LLMs. The ultimate goal is to enhance the Ad Library Political Observatory, a tool we are developing to monitor Meta’s ad business.” That is to say, the images were taken from political advertising on the social network Facebook, as part of an observation process that seeks to make evident the investments in advertising around politics. These are prepared images in terms of what is seen in the background of the image, the position and posture of the characters, the visible objects. In general, we could say that we are dealing with staged images. This is important since the initial information that describes the AI ​​is in itself a representation, a visual creation.