Knowledge in the Dark: Scientific Challenges and Ways Forward

Abstract

We propose the concept of knowledge in the dark – or short: dark knowledge – and outline how it can help clarify why in our current era of Big Data, the knowledge (i.e. evidence-based understanding) of people does not seem to be substantially increasing despite a rapid increase in produced data and information. Key reasons underlying dark knowledge are: (1) the production of biased, erroneous or fabricated data and information and (2) the inaccessibility of data and information, both for example due to sociopolitical or financial interests; (3) the incomprehensibility of data and information, for instance due to specialized jargon and complex disciplinary knowledge; and (4) the loss of previous knowledge due to, for example, the extinction of scientific disciplines. OECD data show that only a fraction of the global investments into research and development are done with the principal intention to increase public knowledge. Most investments are by the industry, steadily increasing with time, whereas governmental investments have been decreasing in relative GDP terms. This pattern suggests an increasing privatization of knowledge. But also in the academic realm, where increasing public knowledge is a primary aim, several factors lead to dark knowledge. We highlight four of these factors – loss of academic freedom, research biases, lack of reproducibility and the Scientific tower of Babel – and offer ways forward to tackle them, for example establishing an international court of arbitration for research and developing advanced tools for research synthesis.

A cultural framework for Indigenous, Local, and Science knowledge systems in ecology and natural resource management

“…Specifically within ecology and related fields, scholars have used the DIKW model in environmental science (Pahl‐Wostl et al 2013, Lokers et al 2016), hydrology (von Asmuth et al 2012), toxicology (McCarty et al 2018), ecological informatics (Flemons et al 2007), forestry (Moffat and Fischer 2013), rangeland management (Karl 2011), and ecosystem planning (Chiang et al 2014, Wang et al 2016). Of particular note for our purposes, Jeschke et al (2019) used the DIKW pyramid to discuss the knowledge–ignorance paradox in the Science knowledge system. Not only has the DIKW hierarchy found utility in Science, but it has also demonstrated an ability to contribute to Indigenous scholarship (Bishop 2019).…”

“…For the purposes of self‐declaration by members of the Science knowledge system to their Indigenous/Local colleagues, it really can be as simple as that. Explanations for why Scientists have so much social difficulty explaining themselves in plainspeak remain elusive (Bensaude‐Vincent 2001, Radford 2011, Smol 2018, Jeschke et al 2019, Janich 2020). Perhaps they just need to get out a bit more, and engage directly with people from Indigenous and Local communities who will simply require that they make themselves plainly understood, not “dumbing it down.” Perhaps these Scientists would discover that the predictive capacities of many Indigenous and Local knowledge systems are in fact quite complementary to the “scientific method” in terms of logic processing, evaluating reliability of knowledge and knowledge holder, and opportunities for cross‐validation of knowledge about the states of Nature (Knapp et al 2011, Santos et al 2020, Saunders et al 2020).…”

“…We recognize the essential role of knowledge brokers within knowledge systems, those who specialize in the reciprocal and meaningful exchange of DIKW (1) between specialized sub‐systems within a knowledge system and/or (2) between knowledge systems at the community or cultural levels (Keszey 2018, Areia et al 2019, Jeschke et al 2019). Within a single culture, Brokers can help to creatively enhance knowledge transfer among individuals in different communities that would otherwise have relatively low probability of successful engagement.…”

The Hierarchy-of-Hypotheses Approach: A Synthesis Method for Enhancing Theory Development in Ecology and Evolution

“…In principle, this increase should make it possible to describe and explain complex systems in much greater detail than ever before. However, an increase in available information does not necessarily correspond to an increase in knowledge and understanding (Jeschke et al 2019 ). Publishing results in scientific journals and depositing data in public archives does not guarantee their practical application, reuse, or the advancement of theory.…”