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To support what I think Arthur is saying I have believed for many years and have seen the academic literature evolve to understand that knowledge is neither tacit nor explicit but instead is a mixture of both and that the mix varies between users. To illustrate: an expert may see their knowledge as mostly explicit and can explain it when asked, however other users who aren't expert see that same knowledge as mostly tacit. I personally believe that there is no purely tacit knowledge and that most all explicit knowledge has a small degree of tacitness, for example, I also teach systems analysis and design and while I can teach architecture concepts and discuss coding techniques and make them quite explicit with rules and heuristics, I still find that there is a tacitness to this as there are concepts I understand from 40 years ago that aren't taught today and haven't been for several years. So when I talk about a piece of code that I developed 30+ years ago to do nuclear containment leak rate testing I can explain all the coding aspects but still find students don't really understand it as I developed that code using compiled basic that required me to define my graphics pixel by pixel, communication protocols had to be expressed explicitly, and data/memory management had to work in a 64k environment. Students don't need to do this now and so while I can explain it exactly, I find that I have to go into much greater detail because while I consider the knowledge explicit, students don't. Same for paper and chapter on why we can't go to the moon, the need for KM. So while many have said that explicit knowledge is really information, in reality it is not always so. So to agree with Arthur, knowledge exists on a continuum with the end points being tacit and explicit and knowledge is a mixture of both. It is why I also said in an earlier post that I group knowledge by that which is harder to extract and that which is easier.
And as a side note, when my nuclear code was used a couple of years ago we had to go purchase older designed computers from eastern Europe to run the code (new computers won't and no one wants to invest the money to upgrade the code and recertify it), another aspect of tacitness that we hadn't anticipated......murray jennex
From: Arthur Shelley <arthur@...>
Cc: Arthur Shelley <arthur@...>
Sent: Sat, Jan 16, 2021 10:39 pm
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
I have always considered categories convenient and efficient, but an oversimplification when it comes to real life matters such as knowledge, relationships and emotions.
I see such things as a mixed "This snd that" continuum rather than either "This or that". In the case of knowledge, this means a gradually shifting balance of tangibles (one bookend of the continuum) through to pure intangible (the other bookend). Most aspects of knowledge & related artefacts have elements of both tangible (explicit if you like) characteristics and intangibles (tacit). Its like light behaving as both a wave and a particle - we come to understand different insights depending on which perspective we adopt.
When I proposed such an insight be part of the ISO30401 KM Standard, it generated considerable dialogue. Although not included in the standard as such, it gets a mention in the appendix. I suggest it is useful to see knowledge as a complex thing that can be in different forms that merge into each other to differing degrees. These aspects are interdependent and are challenging to separate out, but it can be useful to consider the knowledge if interest from a range of perspectives when looking for solutions.
Hope this helps
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On 16 Jan 2021, at 23:12, Stephen Bounds <km@...> wrote:
Hi Chris & all,
I am not a fan of the tacit/explicit dualism because I think it is too reductive and privileges a human-centric view of knowledge. I agree with Nick that it is perfectly fine for something to be both information and knowledge.
Here are just a few scenarios which I think challenge traditional views of tacit vs explicit, and explicit knowledge = information:
Rather than tacit vs explicit, I believe that the key transition occurs as we cross a system threshold. Inside the system threshold, it is meaningful to talk about its knowledge; outside, we must talk about transmitting information. New information can only be accepted as knowledge into that system once it achieves a certain trust threshold.
Thus, a written process sent through by head office represents information received by a staff member, but can be knowledge once incorporated into the execution of their role's systemic practices.
The switch in language between a role and a person is important since it represents an enlargement of system scope; employees are often asked to undertake a role "performance" which includes a broad scaffolding of policies, processes, technology, and person-to-person relationships.
==================================== Stephen Bounds Executive, Information Management Cordelta E: stephen.bounds@... M: 0401 829 096 ====================================
On 16/01/2021 6:02 am, Chris Collison wrote:
I’m with you Fred.
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
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From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
I say the following knowing the risk with which I am placing myself 😊…never been a fan of “implicit” and so I am not a fan of Polanyi’s definition. My point, based on years of practical consulting application, is that “tacit” knowledge can be elicited, harvested, captured and distilled to make it searchable, findable, accessible and usable/reusable. There is a skill and craft involved IMO. My definition of knowledge is based on the concept that it consists of information and experience = knowledge. For practical, not academic or theoretical application, this simple, practical definition I find has meaning in a business or operational environment. This has been my experience.
FWIW, in relation to Bill’s use of “explicit” and “tacit,” I find a third category useful; namely, “implicit.” I define “implicit” as knowledge which hasn’t been articulated but can be. Tacit, by Polanyi’s definition, can’t be articulated. And explicit, of course is knowledge that has been articulated.
When it comes to capturing implicit knowledge, lots of folks have been doing that for many years. You will find them in the training and human performance community.
For more, see a piece I wrote for the KM Yearbook (2000-2001). https://www.nickols.us/knowledge_in_KM.pdf
Fred Nickols, Consultant
My Objective is to Help You Achieve Yours
Been enjoying this timeless discussion on K capture and the extended inference about tacit knowledge and the subtleties of human language in the sharing in the context of the below thread.
Believe it is generally recognized that technology cannot yet get what is one person’s mind and transfer it to another person’s mind. At the heart of the issue, I believe, is the skill set that is required to capture or better said, to elicit what someone knows or can share, to distill and make sense of that knowledge whether it be explicit or tacit, and then to characterize it for reuse within an organization (fit for purpose and context relevant) and then to make is searchable, findable, downloadable, and usable/reusable.
I believe that this is a reasonable approach to as stated below “…to study KM processes, identify patterns and see if technology can address that in a narrow sense.”
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We are still very far from seeing any AI technology that can intelligently extract human knowledge in a broad sense. A main challenge is that of data, and in particular textual data. Data has to be structured in a certain format to be consumed by an AI machine, but in the case of textual data, the number of permutation and subtleties in human language makes this task tremendously difficult. But perhaps the biggest challenge is the limitation of computation power. The most powerful computer today has roughly 1 billion neural connections/synapses, similar to the amount of synapses that a honey bee has vs 100-150 trillion synapses of a human brain. It has been suggested by computer scientists that we are actually just approaching bee-level intelligence, and we are not even there yet.
That said, AI should be sophisticated enough for a focussed application, especially if it’s built around narrow use cases and trained for specific domains. e.g. the technology to recognise and extract names/entities from homogenous, unstructured text is fairly advanced, and so is the ability for a machine to auto-index/classify documents and textual data. Information extracted as such, combined with data visualisation techniques, will go a long way in enhancing the comprehension of what’s contained in an organization's textual data, and that is a possible KM angle you can look into.
Instead of looking at broad ways “to extract automatically all types of knowledge in an organization” (I don't think this exists), it might be worthwhile to study KM processes, identify patterns and see if technology can address that in a narrow sense.