Bruce King’s Post

https://lnkd.in/gC9bUbaK This article highlights a significant limitation to research - solutions sell. Be it a genuine commitment to solving scientific problems or maintaining a scientific ego, publishers seek positive results. Distinguishing "positive results" from "negative results" can become ambiguous, and unfortunately, the author does not provide definitions. Let's dive in. 🤓 "Positive results" often refer to results that confirm a hypothesis, whereas "negative results" usually refute that hypothesis. However, this nomenclature infers a "good and bad" dynamic. Those of us who study nuance understand that the relationship between "good" and "bad" is, well, nuanced. It's easy to fall victim as authors. While I agree that the shift towards "negative data" publication is useful, at the very least, we must find means to publish in the meantime. As authors, we can own our fate by re-contextualizing data for publication by these means: 1. Experimentation. Results propagate questions. In other words, whether a result confirms or refutes a hypothesis, a follow-up question can be manufactured. We may just have to change our biases. The most straightforward approach may, therefore, be to find a useful lens and execute a new experiment. (Psst - this is a great time to execute humility and incorporate collaboration. Ask your community for help!) 2. Text. Negative results are not negative by nature. Instead, the results are likely considered out of context. It may be that the interpretation and discussion of the results need reworking with new considerations in mind. Instead of "ABC does not cause XYZ," reworking the text to "XYZ occurs independent of ABC" could read as more "positive." This especially applies to hypotheses and titles of papers. Lingual restructuring becomes easier over time and with more experience, so it is never too early to begin practicing. Be careful not to simply convert active to passive voice. 3. Structure. In academia, experiments are often not performed in the order they are presented to the reader. It is ultimately the author's duty to decide the order in which data is presented. This ties directly into contextualization. Is your justification for performing the study better used as an interpretation? In other words, can the vantage point of the experiments be changed for the sake of publishing the results? It's important to note that none of these suggestions involve data or statistical manipulation. The goal of each of these suggestions is not to manipulate; rather, it's to publish experiments to help each other promote knowledge. Happy writing!

🌟 Ready to unlock the secrets of complex systems? 🌟 Here’s an "Introduction to Complexity" by the Santa Fe Institute and Melanie Mitchell. Imagine a flock of birds effortlessly navigating the skies. 🦅 Their simple actions create a harmonious, complex system. This course unpacks how individual elements interact to form intricate networks, mirroring real-world systems in business, ecology, and beyond.🌱 🚀 Ready to explore? Check out the course: Introduction to Complexity https://lnkd.in/dKeejQdk #systemsthinking #complexitytheory #sustainability

Let me know how this works as a blurb for my book Extinction Event: Extinction Event starts with a middle-aged guy known as LP going back to school to study music only to discover a massive, global shift that will happen as a result of a sentient artificial intelligence known as GAIA. Through the book, we meet friends and foes of LP and various villains trying to prevent human progress with issues like climate change, animal extinction, political issues and more. The book ultimately forces the reader to realize that we're all on a very dangerous path and whether or not AI will save us or destroy us. The book was written specifically for the fast-growing #climatefiction category, but can also be described as a #thriller, #mystery and #dystopian fiction.

I have been diving deep into systems science lately and I just came across Nexus: A Brief History of Information Networks. This book explores how information networks have evolved—from ancient systems to today’s digital age. It’s very fascinating to see how these connections have shaped the world we live in.

From artificial-intelligence algorithms to zebrafish, this book take a precautionary approach to assessing how sentient such entities are. https://lnkd.in/d7XpwQ4E

Calculating Empires A Genealogy of Technology and Power Since 1500 This website and project is a profound and ambitious attempt to map out the intricate intersections of technology, power, colonialism, and control over five centuries. It’s an intellectual deep dive into how technical systems and social structures co-evolve, often reinforcing hierarchies of dominance and exploitation. The visual and thematic approach, spanning communication, computation, classification, and control, feels like an important and necessary tool for understanding not just where we are today but how we got here. The genealogy it presents makes clear that our contemporary technological systems aren’t neutral innovations; they’re deeply political, drawing from centuries of colonial practices, resource extraction, and militarized infrastructures. Key themes like enclosure, the commodification of human bodies, and the entrenchment of surveillance and algorithms echo current fears about AI, data monopolies, and environmental degradation. Yet what stands out most is the way Calculating Empires challenges solutionism and demands that we take the time to understand the historical depth and complexity of these systems before imagining alternative futures. The call to reflect slowly on patterns over centuries is refreshing in a world that often prioritizes speed over depth. It’s both a critical act of resistance and an invitation to action. #TechnologyAndPower #GenealogyOfTechnology #Colonialism #DataPolitics #AI #HistoryOfTechnology #SystemsOfControl #Surveillance #Decolonization https://lnkd.in/eYbzgJyT

Using Vague Language About Scientific Facts Misleads Readers https://lnkd.in/dmVZt75s

As a technologist deeply engaged in the intersection of #innovation and #society, I recently discovered an fascinating resource that I believe is essential for anyone seeking to understand the interplay between #technology and power structures throughout history. "Calculating Empires: A Genealogy of Technology and Power since 1500" by Kate Crawford author of the seminal #AtlasOfAI, is a meticulously crafted visual narrative that transcends the often ahistorical nature of tech discourse. This remarkable infographic weaves together timelines of ideas, infrastructures, and systems of power, illuminating the symbiotic relationship between technological advancement and societal control over the past five centuries. The visualization's vertical axis represents time, while the horizontal plane explores four critical domains: communication, computation, classification, and control. This multidimensional approach allows for a nuanced exploration of themes, eras, and concepts across various vectors. Compiling hundreds of illustrations and texts, the work reveals patterns of automation, militarization, colonialism, and resource enclosure since 1500, offering valuable insights into the cyclical nature of technological progress and its societal implications. As we stand on the precipice of an #AI-dominated future, understanding this historical context is crucial. By examining how past civilizations leveraged technology to shape societies, we can gain valuable insights into the potential trajectories and consequences of our current technological revolution. This reflection is critical as we, as a nation and global community, grapple with the development and deployment of AI and other disruptive technologies. The choices we make today will determine whether the future shaped by these technologies will be miraculously or catastrophically transformative (or perhaps most likely a combination of both). Considering the historical patterns illustrated in "Calculating Empires," can help us make more informed, ethical, and far-sighted decisions about the role of technology in shaping our collective future. #AI #TechHistory #DigitalEthics #SocietalImpact #EmergingTech #Futurism #strategicforecasting Explore the visualization here: https://lnkd.in/eyEaF9mq

Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else

So very much endorse Sam's point here. All too often, in many social sciences, we take one study's findings as universal truth and fail to let a thousand flowers bloom. Many of our journals and reviewers search for "novel" theoretical or empirical advancements when rigorous work in science requires incrementalism, replication, and the steady building of evidence across time and disciplinary perspectives. Collaboration and transparency should be the ethical standards rather than gatekeeping and competition. We allow these pathologies to develop through perverse norms and poor mentorship. I hope that we can reverse some of these trends as data becomes more open and formerly abstruse methods and coding languages become more widely accessible through technological innovations like generative AI and machine learning. Then we can think about actual issues more critically and focus on theory development without pressuring young scholars to be fundamentally "novel" at every turn.