II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Twintelligent Gaiable Knowledge

B. The Spiral of Science: Manican to American to Earthicana Phases

Baaquie, Belal and Frederick Willeboordse. Exploring Integrated Science. Boca Raton: CRC Press, 2010. A large, illustrated volume by National University of Singapore physicists wherein 24 chapters – Universe, Numbers, Energy, Atoms, Molecules, Fluids, Materials, Polymers, Electricity, Odor, Sound, Photosynthesis, Vision, Biopolymers, Proteins RNA, DNA, Information, Nano, Complexity, Evolution, Relativity, QM I, QM II –each pose a question to engage the reader. For example ‘Information’ asks how do children come to resemble their parents. And notably as a contribution from a non-Western milieu, an inherent self-organization is tacitly allowed which impels and informs an emergent cosmic vitality.

Rather that the number of genes of an organism, it is the nonlinear manner is which genes interact that gives rise to how complex the organism is. (459)

Bainbridge, William Sims. The Evolution of Semantic Systems. Roco, Mihail and Carlo Montemagno, eds. The Coevolution of Human Potential and Converging Technologies. Annals of the New York Academy of Sciences, 2004. A Director of Information and Intelligent Systems at the National Science Foundation discusses the formation of a “cultural genetics” as a way to understand knowledge concepts and relationships in an evolutionary context.

Today, however, we have the research tools and analytical concepts necessary for a broad reorganization of science as a comprehensive model of reality based on a single set of principles. Fundamental to this vision are the unity of nature, the behavior of matter at the nanoscale, and the evolution of meaningful patterns in complex, self-organizing systems. (151)

Ball, Philip. Triumph of the Medieval Mind. Nature. 452/17, 2008. Drawing upon his new book Universe of Stone: Chartres Cathedral and the Triumph of the Medieval Mind, (London: Bodley Head) the British science writer contends that the historic intellectual revolution of natural philosophy, whence the extant world can be understood as reliably comprehensible, a valid object of study sans capricious divine intervention, occurred firstly in the early 12th century, much before the Renaissance. Thus began the contentious dichotomy between faith and reason, given revelation and found phenomena, still unresolved to this day.

Banzhaf, Wolf and Nelishia Pillay. Why Complex Systems Engineering Needs Biological Development. Complexity. 13/2, 2007. Computer scientists from the Memorial University of Newfoundland, and the University of KwaZulu-Natal, South Africa, contend that a more effective approach than prior top-down control and design is to draw upon natural evolutionary phenomena such as nonlinearity, self-organization, and adaptation which focus more on dynamically emergent processes than fixed, preconceived states.

Barman, Kristian, et al. Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models.. arXiv:2501.05382. We cite this entry by twenty two investigators in the Netherlands, Spain, Germany, Switzerland and Austria because it describes a science spiral practice that blends a title array AI neural net procedures. In regard, into the 2020s global research projects could then be seen as more and more taking off on their own course. See also Automating the Search for Artificial Life with Foundation Models by Kumar, Akarsh Kumar, et al at arXiv:2412.17799.

This paper seeks to scope out the development and evaluation of physics-specific large-scale foundation AI models, which we call Large Physics Models (LPMs).. LPMs can function independently or incorporate specialized tools, including symbolic reasoning modules, analyse specific experimental data and synthesizing theories and scientific literature. In regard, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Finally, Philosophical Reflection views the broader implications of LLMs in physics and what novel collaboration dynamics might arise in research. (Excerpt)

Bauer, Amanda, et al. Petabytes to Science. arXiv:1905.05116. We cite this 80 page, 27 author posting including Alexander Szalay as an example going forward of a worldwide, collaboration necessary to handle data inputs at this 1 million gigabyte scale. Akin to the USA Astro2020 project (herein) its cosmic vista surveys planetary systems, stellar evolution, messenger astrophysics, galactic clusters, fundamental physics and about every aspect as our yet unknown and unnamed person/sapiensphere begins to carry out the universal self-quantification that a genesis procreation seems to require. The quotes describes a meeting held in regard.

A Kavli foundation sponsored workshop on the theme Petabytes to Science was held in February 2019 in Las Vegas. The aim of this workshop was to discuss important trends and technologies which may support astronomy. We also tackled how to better shape the workforce for the new trends and how we should approach education and public outreach. This document was coauthored during the workshop and edited in the weeks after. It comprises the discussions and highlights many recommendations which came out of the workshop. We shall distill parts of this document and formulate potential white papers for the decadal survey.

Bekoff, Marc. Redecorating Nature: Deep Science, Holism, Feeling, and Heart. Science. 50/8, 2000. An insightful editorial by the University of Colorado cognitive ethologist calling for a kinder, gentler sensitivity.

Whereas we’ve certainly leaned much about nature, traditional reductionistic science often falls short because it fragments the world. Reductionistic science disembodies and dissects wholes into parts. What results are views of the universe in which dynamic, multidimensional interactions are presented as static, dimensionless flatlands.
I believe holistic and heart-driven science is needed, deep science that is impregnated with spirit and compassion. Holistic, heartfelt science reinforces a sense of togetherness in which seer and seen are one. It fosters the development of deep and reciprocal relationships among humans, other animals, and other nature, softening our tendencies to control and manage almost everything in sight. (635)

Beller, Mara. Quantum Dialogue. Chicago: University of Chicago Press, 1999. An historian of science describes how the establishment of a scientific paradigm, in this case quantum physics, occurs through constant argument between proponents of competing viewpoints. But the winner may not always have the best solution. The wave/particle complementarity of the Copenhagen school prevailed due to the conversational persuasion of Niels Bohr in dialogue with Albert Einstein, Werner Heisenberg, Wolfgang Pauli and others than to its mathematical basis. Beller then deftly shows that Bohr came to this theory because of its affinity to the ancient metaphysical analogy of microcosm and macrocosm and the yin/yang reciprocity of Chinese wisdom.

Bettencourt, Luis and David Kaiser. Formation of Scientific Fields as a Universal Topological Transition. Santa Fe Institute Working Papers. 15-03-009, March, 2015. SFI and MIT physicists achieve a significant synthesis for several reasons. From our late global vantage the historical course of science is now arrayed altogether so that independent, common patterns can be found. A “general theoretical framework” is thus described in terms of complex, self-organizing, modular systems. With this in place, an affinity to the physical phenomena of statistical, condensed matter mechanics becomes evident, so as to root in the natural cosmos.

Scientific fields differ in terms of their subject matter, research techniques, collaboration sizes, rates of growth, and so on. We investigate whether common dynamics might lurk beneath these differences, affecting how scientific fields form and evolve over time. Particularly important in any field’s history is the moment at which shared concepts and techniques allow widespread exchange of ideas and collaboration. At that moment, co-authorship networks show the analog of a percolation phenomenon, developing a giant connected component containing most authors. We develop a general theoretical framework for analyzing finite, evolving networks in which each scientific field is an instantiation of the same large-scale topological critical phenomenon. We estimate critical exponents associated with the transition and find evidence for universality near criticality implying that, as various fields approach the topological transition, they do so with the same set of critical exponents consistent with an effective dimensionality d ≃ 1. These results indicate that a common dynamics is at play in all scientific fields, which in turn may hold policy implications for ways to encourage and accelerate the creation of scientific and technological knowledge. (Abstract)

Scientific fields are self-organizing collections of people, their knowledge and interactions, and the physical products of their research. (7) Under the usual scaling hypothesis, long familiar in statistical mechanics for accounting for finite-size effect in critical phenomena we find suggestive evidence that all nine of the scientific fields under study are finite-sized realizations of a single, idealized, infinite-volume transition. All nine scientific fields, in other words, appear to be members of the same universality class, governed by a single set of critical exponent. (7) The existence of a general theory and detailed model that describes the formation of scientific fields across disciplines, time, and population size would provide a new comprehensive, quantitative, and predictive framework with which to understand the social and conceptual dynamics that drive the self-organized creation of scientific communities. (7)

Bettencourt, Luis and David Kaiser. Formation of Scientific Fields as a Universal Topological Transition. Santa Fe Institute Working Paper. 2015-03-009, 2015. For this new 2015 section to report growing evidence of a natural universe to human genetic-like reiteration, Santa Fe Institute and MIT physicists post one of the strongest affirmations to date. As other entries, this is accomplished by a cross-integration of statistical mechanics and complex systems science, so as to achieve a widest span from physical substrates to human inquiry. As an exemplary instance, even worldwide research studies from cosmic and quantum realms to medicine and nanotechnology are amenable to study this way because they exhibit the same dynamic patterns as each and every scale and instance. Thus basic, condensed matter phenomena such as densification, percolation, universality classes, and so on, are manifestly present as science proceeds to similarly self-organize. These lights contain a section as “Non-equilibrium phase transitions and the dynamics of discovery,” and the concluding second quote. A significant document also at arXiv:1504.00319.

Scientific fields differ in terms of their subject matter, research techniques, collaboration sizes, rates of growth, and so on. We investigate whether common dynamics might lurk beneath these differences, affecting how scientific fields form and evolve over time. Particularly important in any field's history is the moment at which shared concepts and techniques allow widespread exchange of ideas and collaboration. At that moment, co-authorship networks show the analog of a percolation phenomenon, developing a giant connected component containing most authors. We develop a general theoretical framework for analyzing finite, evolving networks in which each scientific field is an instantiation of the same large-scale topological critical phenomenon. We estimate critical exponents associated with the transition and find evidence for universality near criticality implying that, as various fields approach the topological transition, they do so with the same set of critical exponents consistent with an effective dimensionality d≃1. These results indicate that a common dynamics is at play in all scientific fields, which in turn may hold policy implications for ways to encourage and accelerate the creation of scientific and technological knowledge. (Abstract)

Scientific fields are self-organizing collections of people, their knowledge and interactions, and the physical products of their research. These collections evolve over time, seemingly in quite different ways. (7) These differences mask important underlying similarities in how scientific fields grow and develop. (7) The scaling formalism assumes self-similarity: near
the critical point, any portion of the system, if magnified to the size of the entire system, should behave indisinguishably from the entire system itself. (7) The existence of a general theory and detailed model that describes the formation of scientific fields across disciplines, time, and population size would provide a new comprehensive, quantitative, and predictive framework with which to understand the social and conceptual dynamics that drive the self-organized creation of scientific communities. (7)

Bettencourt, Luis, et al. Scientific Discovery and Topological Transitions in Collaboration Networks. Journal of Informetrics. 3/3, 2009. With coauthors David Kaiser and Jasleen Kaur, in a special issue on the “Science of Science,” SFI, LANL and MIT scholars distill a persistent iterations of dynamic interconnections as neuron-like scientists proceed to fill in the cognitive workspace of their research endeavors. A generic neural network geometry thus gains ubiquity across societies, innovations, written texts, and so on. Indeed, circa 2009, it can be alluded in summary that such learning associations appears as a natural arrangement from genomes to Broadway musicals.

We analyze the advent and development of eight scientific fields from their inception to maturity and map the evolution of their networks of collaboration over time, measured in terms of co-authorship of scientific papers. We show that as a field develops it undergoes a topological transition in its collaboration structure between a small disconnected graph to a much larger network where a giant connected component of collaboration appears. As a result, the number of edges and nodes in the largest component undergoes a transition between a small fraction of the total to a majority of all occurrences. These results relate to many qualitative observations of the evolution of technology and discussions of the “structure of scientific revolutions”. We analyze this qualitative change in network topology in terms of several quantitative graph theoretical measures, such as density, diameter, and relative size of the network’s largest component. (Abstract, 210)

We show that regardless of the detailed nature of their developmental paths, the process of scientific discovery and the rearrangement of the collaboration structure of emergent fields is characterized by a number of universal features, suggesting that the process of discovery and initial formation of a scientific field, characterized by the moments of discovery, invention and subsequent transition into “normal science” may be understood in general terms, as a process of cognitive and social unification out of many initially separate efforts. (Abstract, 210)

It is also possible, as suggested by Kleinbergand others, that simple patterns in text – for example, the anomalously high occurrence of certain words or word combinations – can be correlated with changes in network properties, helping to make the case for the simultaneous social and conceptual cohesiveness of a field promoted by the discovery of new concepts or techniques. Such a finding would help establish the generality of the cognitive and social mechanisms that underlie all processes of discovery and innovation and, in the process, give us universal models that capture the essence of their statistics and dynamics. (220)

Biswal, Bharat, et al. Toward Discovery Science of Human Brain Function. Proceedings of the National Academy of Science. 107/4734, 2010. In a dedicated effort to bring to neuroscience the powerful methods by which genomes are catalogued by international consortiums, some fifty-three authors from institutes around the world explore how such a project might proceed. And I have seen it stated that the human brain will never be able to understand itself. But could not this global expanse of cerebration, as if an emergent worldwide mindkind, be seen to in fact as achieving just this profound decipherment?

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