Ignorance of Ignorance and Meeting the Needs of the People
Casual ignorance vs selective discovery with emphasis on natural human abilities
With Miroslaw Manicki
I had a friend in college who was dating a girl that was going to be trouble. One of his roommates said that he should call the relationship off. He responded, “Sure, I’m in a rut, but it is a fur-lined rut.”
Selective ignorance
It isn’t to say that this was a case of ignorance, but it demonstrates a kind of passiveness that translates to the idea of how much personal ignorance one can tolerate. There is comfort in this. The following image “The ‘Pizza’ of Knowledge and Ignorance” demonstrates to a degree this concept of selective knowledge and ignorance. This underscores the importance of understanding and making use of conceptual space.
Being selective about knowledge and living with certain levels of ignorance is a challenging prospect. Do we have a knowledge problem in our world? Decidedly not. We have a distribution problem, and the absence of knowledge is the medium through which knowledge must flow.
It is so difficult to arrive at knowledge in a particular case, we learn to cozy up in our own little cubbyholes, or fur-lines ruts, of ignorance. It is true that ignorance isn’t bliss, but it can become comfortable, to our detriment. It isn’t a given that ignorance is a bad thing. There are many things that you could rightly choose not to know.
Ignorance plays into our daily lives more than we typically recognize. As can be seen in the following year 2000 publication by Scott Adams, “When Did Ignorance Become a Point of View?” This was a ridiculous proposition when published by Mr. Adams, but not so now. Ignorance is not only the important perspective, but it has become leveraged and expanded upon, layer after layer after layer.
Surprisingly, to me at least, ignorance as a phenomenon is the subject of a good deal of serious scientific study. In this sense, it is called agnotology. This kind of ignorance — ignorance as lack of knowledge — is to be contrasted with the layered, purposive networks of lies, intended as a viable alternative to knowledge.
Early in my career, I was in finance. In some cases, it is common in that field to pay for a legal opinion or other investigative study on a subject important to a project, but difficult to understand without guidance (Wikipedia contributors, 2023). This might involve questions of whether a certain kind of asset or transaction would be taxable or otherwise regulated. There might be estimates as to the potential marketability of a program or asset type. It is common to sometimes pay hundreds of thousands of dollars to law firms or other professionals to come up with an opinion on one or a few questions.
How much ignorance can we live with? What options exist for us to be able to gain knowledge in certain important areas. Before deciding, let’s consider a studied approach to ignorance that may help us. Agnotology relates to many kinds of ignorance. Jennifer Croissant (2014, 6) lists five underlying issues:
- Ignorance considered as presence or absence of knowledge. This is as we have just considered.
- Time-related issues related to this. Croissant refers to this as chronicity.
- Granularity, the “texture” of ignorance — the details. We would insert here the importance of understanding the process approach as fundamental. This has to do with the progress of phenomena over time, given that there is always flux, or change.
- Scale, independent of granularity. Do phenomena exist on a grand scale, with large things, or with the small. This is best understood, also in a process context, in the levels of living systems by James Miller.
- Intent, whether pernicious or not. There are agents and provocateurs of ignorance to the point of falsehood, the question of knowledge promotion takes on political implications.
Given that the act of gaining knowledge is expensive, time consuming, and difficult, a fundamental question is how much ignorance can be lived with. This is true of questions related to health and to our interactions with nature generally — as if we are not an intrinsic part of nature.
Knowledge is about people, and it is multifaceted
Herein, I address a specific kind of ignorance: The lack of knowledge with respect to one’s own abilities. This kind of ignorance levies considerable penalties on all of us in that we fail to achieve our potential and gain fulfillment to the degree that we cannot make use of our most unique and pronounced abilities. This shortfall also penalizes society, which cannot thus enjoy the benefits of the talent and genius of many of us.
I have a friend who said that personal enemies are cumulative, while close friends come and go. He meant this as a joke, but a similar paradox exists with respect to most abilities. Knowledge of abilities is often sketchy. There is no formal effort to bring abilities to light. Conceivably schools would aid in this process. They provide excellent support in areas of cognition — considering analytical tasks, mathematical questions, mostly phenomena represented by numbers of some kind.
Is it possible to know it all? Of course not, but we can strategically consider the questions of knowledge and expertise in ways that leverage knowledge and talent generally through cooperation and individual discovery. Knowledge is best understood as a process. Knowledge of a particular state of affairs can be achieved by considering underlying conditions using a tapestry of requirements — a tree-based model in fact — to match such conditions with known relationships (Tingey, 2009/2018).
This all can be conducted by communities of practice (what Tönnies referred to as Gesellschaft) matched up with living communities (Gemeinschaft as per Tönnies), intermediated by technology. It is critical that technology not impose itself on such a dialog, nor sideline or corrupt it in any way (Parsons, 1937).
Knowledge is typically thought of a static state — the awareness of a fact. This is a shortsighted and largely inaccurate perception. The question is in arriving at an informed state, which has more to do with process than in a static condition (Whitehead, 1929/1979). If a person can learn how to learn — and learn about what they need to learn — knowledge can be readily obtained in many cases. This applies to networks and communities of people as well to the degree that they have tools and technologies available to them to bridge space and time in doing so (Tingey, 2014).
That is what the AI people have long announced that they are attempting — at least for their technological offspring. In order for their products to work as promoted, the products must be based on some kinds of comprehensive knowledge models. The onus is on proponents of AI to describe how that enterprise would coexist with human knowledge production. They don’t try very hard. Similarly, rationalization of AI at all is mostly of a “gee whiz” category, ignoring the gaping chasm of technology shortfalls in reflecting existing, proven knowledge.
When did people stop thinking? When did we run out of ideas in the first place? The most prevalent concept from the AI world is that they would take knowledge production and use from the people, the scientists, practitioners, policymakers, etc. (Kurzweil, 2005). They apparently see no problem in this. This is a stunning and frightening example of the Dunning-Kruger Effect run amok. They are not in the least aware of their ignorance. This phenomenon will be considered presently.
The need is to be able to expand on human capacity to arrange and rearrange matter and energy in conceivable and beneficial ways. As knowledge is messy, comprehensive, and overlapping, organizing its production and use is a monumental task.
Allen Tingey Farnes and Millington (ATFM) abilities model
One major problem with AI is that knowledge isn’t singularly cognitive; it isn’t fully logical. Knowledge encompasses the whole of human experience, all of our abilities, all of our senses, all of human phenomena. With colleagues we made an academic presentation on the subject in 2010, showing abilities aspects including cognition, perception, sensory means, affective, physical, psycho-motor, and personality. Each of these represented many sub-categories, about fifty abilities categories by this count. Extending the analysis, we identified over two hundred categories of abilities. Each of these involves abilities, which lead to knowledge forms.
Here is the model of our various natural abilities from that presentation. ATFM stands for Allen, Tingey, Farnes, and Millington, the team of collaborators. We brought the model together by adding together the work of Edwin Fleishman (Fleishman, and Reilly, 1992/2008), Robert Sternberg (1988, 2000a, 2000b), Paul Guilford (1967), Howard Gardner (1983, 1999), the International Classification of Functioning, Disability, and Health (ICF) (World Health Organization, 2001, 2003), and the US O*NET occupational abilities classification model (Employment and Training Administration. 2002a, 2002b).
Guilford and Fleishman were committed to not only describe abilities categories, but to also provide defensible tests to identify gradations of such abilities in individuals. The famous standardized tests used in scholastic and academic circles to study cognitive abilities are examples of what could be carried out in all ability areas, coupled with support and targeted education and training.
Narrow as they were, standardized testing of a broader nature was introduced in the 2000s. Robert Sternberg, a leading scholar in this area and the author of expanded SAT testing for analytical, practical, and creative skills (Sternberg et al., 2003). In consultation, he provided me with private research materials used in Standard Aptitude Test, SAT development where he considers the three major aspects of his testing model, which is top center in the prior figure.
His program was to consider certain combinations of cognitive and psychological abilities, how they came together to predict their performance in school, if not in a competitive work environment. He refers to this as “successful intelligence” (Sternberg et al., 2003, 7). These involve “analytical intelligence”, “practical intelligence”, and “creative intelligence”.
Following is a model of how knowledge is gained, maintained, and aggregated. This is a product of a 2010 conference presentation I presented with This a combination of abilities areas for which there are supportable tests. Psychologists and others in the abilities detection field depend on such tests, which are administered for many reasons. Some are standardized and utilized for access to educational resources and for occupational decisionmaking. As to the following model, we consider it as background for efforts at expanding the scope of such activities.
Meta analysis — thinking about thinking
There are three important factors that relate to the question of relevant knowledge. One is situational overconfidence spurred on by systematic ignorance. Next is the implication of the effect on one’s own native abilities. Lacking experience and perspective, an individual — and collections of people — can suffer from ignorance of their own native abilities and capacities. Lastly are governance shortfalls from colletive ignorance on these points. This is a timely question given the vast public effort to shortchange human knowledge production and use without even an argument as to the benefits of reinforcing human knowledge production and use in lieu of machine-based conjecture.
- Situational overconfidence
One is described as a condition of situational overconfidence. This has been termed the Dunning Kruger Effect (Kruger and Dunning, 2009). As seen below, the effect reflects the implications of a state of ignorance in that a lack of knowledge can tend to be blind to all else. This is referred to as ignorance of ignorance.
As seen in the figure, the lack of knowledge about unknown phenomena can result in overconfidence. As a person is exposed to the new area, confidence can be dashed as one faces the reality of it. This can be overcome with learning and effort. Ultimately, competence can be gained through learning and effort.
Of course, another reaction to the situation is to ignore that which one does not know — relegating it to the realm of the “other”, by which token that outside reality is not embraced, not acknowledge, and possibly feared and ignored. This is a state of unsanity as described by Alfred Korzybski by which the person or persons in question are wholly left to the ravages of what it is that they have ignored or rejected (Korzybski, 1921).
Facing up to found levels of ignorance is the essence of learning and of improvement. In a complex, interrelated world in which everyone cannot master everything, this must needs be a world of sharing and of mutual dependence. Trust and empathy are critical factors in such a world.
2. Ignorance of one’s own abilities, particularly talents and gifts
Another problem is a condition under which a person is ignorant of his or her own personal abilities. This condition is less well defined in relevant literatures; it comes from our experience studying the breadth of natural abilities as per the Allen Tingey Farnes Millington (ATFM) model.
As seen below, a person can subsist in blissful — or painful — ignorance of one’s own abilities. Indeed, this can be the case simply because the person in question does not notice that carrying out certain tasks are relatively easy, not noting that they are difficult for others. Without some kind of test or observation by someone in a position to know, such ignorance can last a lifetime. This can truncate that persons opportunities, including occupational and career opportunities, means of gaining satisfaction, and social standing and advancement.
How does a person sort through all of these options to come to understand which of them apply to a particular case? Think of Beethoven without music theory and notation or Newton without a Cambridge to identify and hone his skills.
There are key factors: Identification of the areas of abilities, including strengths and weaknesses; inclusion in a supportive community of like-minded and skilled collaborators and mentors; education and training in associated information and processes; and further inclusion in lifelong communities of practice for compensated work, service, enjoyment, and fulfillment. This kind of activity can be best understood under the rubric of social network analysis (Wasserman and Faust, 1994) and network analysis generally (Barabási, and Pósfai, 2016).
In our analysis, there is a difference between skill and ability. Some who is skilled is trained. A person with an ability may not be — nor may they be even aware of the nature of the activity in question. Many people are routinely trained in areas outside of their relative endowment of natural ability. This isn’t to say that they cannot fulfill important roles.
This raises an important issue; think of the dynamic where a trained person with standing and influence encounters another with raw ability in the area in question, or little influence and standing. They may well subordinate, ignore, or alienate them from relevant communities and activities. Think of the famous movie Amadeus where the jealous Antonio Salieri hurts the chances of a more gifted Wolfgang Mozart, who he also punishes out of spite.
We propose a facility as part of the educational system with the specific purpose of identifying natural abilities in the populace, conceivably in early elementary school, in middle school, in high school, in vocational and academic environments, and in occupational programs for adults.
Following is a form that can be used to compare ability profile features of individuals — whether from valid testing or opinion and observation. Representing the roughly fifty ability areas referenced earlier, they each represent many further aspects and considerations. These are best considered within the communities themselves.
Self-assessment or evaluation of loved ones, associates, and acquaintances. These may be helpful, but not necessarily so. It is possible that such assessments are useful. The idea is to identify the person in question and to colorize the check boxes to come to easily interpret the ability profile in question. I recommend the color green for talents and gifts and red for weaknessess and disabilities. Any other color might suffice for typical assessments. Tan works particularly well for this level.
Color groupings can thus help to identify abilities patterns. Matching these to career, occupational, and educational commitments is fundamentally wise.
Respect for the abilities identification program calls for meaningful and valid testing and identification programs. The ATFM effort is to identify and activate these such as they exist. Guilford identified 180 categories, emphasizing cognitive and psychological elements (Looti, 2023). Fleishman identified 52 categories, including more physical, psychomotor, and sensory aspects (Fleishman and Reilly, 1992/2008).
3. The effects of situational ignorance in governance
How can society as a whole deal with strengths and weaknesses of existing and potential leaders? The Dunning-Kruger Effect manifests itself in the governance of organizations, in the strengths and weaknesses of the people call, head, hand, heart, and avoiding institutional ignorance.
The following figure is a mockup clarification of relative strengths in the seven abilities categories with respect to head, hand, heart implementation model. There are various areas of strength and vulnerability in that model under different abilities profiles.
For example, “head” approaches would affect cognition to be sure and some other categories of psychomotor and perception ability, but less with respect to personality, affect, and sensory abilities. “Hand” would tie in to psychomotor and physical abilities, but not necessarily in areas of perception, personality, nor cognitive ability. “Heart” approaches cover much space with regard to sensory, affective, and personality trait abilities — filling gaps that would otherwise introduce risk to governance and program execution (Scherkenbach, 1990).
Naturally, there are connections between “head” issues and cognitive abilities, “heart” issues and affect, and “hand” issues and physical and psychomotor. These are designated by placement of the three figures in the Ability Profile itself.
It is possible for individuals to have balanced abilities profiles in their own right, covering questions of head, hand, and heart. It is a better bet that some combination of people are better suited to broader perspectives, with less likelihood of blind spots and a more comprehensive footprint such that risks are mitigated and rewards are more likely to emerge.
Head only effects in governance
In some sense, governance has been considered to be a cerebral activity. This has largely been born out, but with significant caveats. Conceivably, finance has benefited, but at the same time, significant questions have been raised when considering broader views. As seen below, shortfalls in six of the seven abilities categories can exist in a cognitively led program.
Dell Allen, who stimulated this study in the first place, offered at the time his opinion that economic policy is likely carried out by individuals lacking in empathy. There are myriads of ways for shortfalls to exist when logic is the only guiding factor in decisionmaking.
Hand only effects in governance
With only hand, there can be capacity, but it can be poorly positioned. For one thing, negotiating capacity can be found wanting.
Hand-centric efforts can be blindsided many ways, as well. It can also be wanting in generating desire, not only in planning, managing, and evaluating, but with respect to the desirability of the product/service in the first place. A person in this category would find himself/herself working hard, but for little reward.
Heart only effects in governance
Passion will serve to stimulate activity, but it may not allow for sustained delivery of quality and adaptability to change. Well-meaning people will not be able to provide sustained outcomes.
Heart-centered approaches may make for difficult adjustment to change, particularly where difficult decisions with respect to existing relationships are needed.
Combined abilities frameworks regarding head, hand, and heart as seen below provide for broad-based opportunities in individual development and organizational governance generally. A natural endowment in all of the seven abilities areas provides for a broad-based life, considering both work and lifestyle aspects. Conceivably, people who score high in many abilities areas can enjoy a good deal of latitude in their choices.
When considering the cumulative effect of organizational governance and the effects of group effort, it is beneficial to enjoy a balanced mix of individuals in leadership to consider a broader spectrum of possibilities. Among other things, this allows for higher leadership levels. Better ability levels in one aspect can bring better outcomes and mitigate risks in those areas. Teams with imbalanced portfolios that mirror one another do not enjoy such benefits and face more risk.
How we can use our abilities to extend our knowledge
Gaining deep knowledge is a holistic affair, integrating human cognitive and sensory systems via abilities as presented earlier. The material was put together by Bogdan and Biklen (1998/1992/1982). They outlined the various means of gaining knowledge from both a qualitative framework and quantitative means.
Qualitative methods are found on the left with white backgrounds and quantitative methods are on the right with white backgrounds. Quantitative research constitutes a effort at precision and validity in a few cases while qualitative research is concerned principally about meaning. In some cases, these are at odds, but they can prove highly complementary and useful.
Lay people associate scientific investigation with formulas and numbers — the quantitative side of science. This is typically the white-lab-coat-kind of investigation. Such quantitative means can be found in the shaded area to the right of the figure below. In a broader sense knowledge formation extends to all aspects of human life, incorporating all abilities categories and the multitude of ways that people interact with each other and with nature generally.
Quantitative study can be precise, but it can often be difficult to deduce meaning from numbers. Much quantitative scientific work considers probabilities of whether something does or does not manifest itself. Certain inferences or suppositions can be forms to describe this, but these have limited interpretive groundings. By means of association or correlation, rigorous quantitative relationships can be guessed at as to meaning or implication. This is a weakness of much of science.
Qualitative study considers meaning and networks of meanings that help investigators to derive meaning from conditions and relationships even if they are less effective at understanding detailed relationships and engaging in prediction. As seen in the prior figure, the broader view of scientific study includes cognition as is widely known, but also perception, sensory abilities, affective capacities, physical abilities, psychomotor abilities, and personality traits. These are all valid. They all lead to expansion of useful human knowledge, both with respect to individuals and combinations of people.
Here is a second-level representation of knowledge-generation methodologies. These further support the concept of breadth in learning efforts. Techniques and methods as described can often be integrated with the act of doing itself, alone or in groups. This supports a positive cycle of learning and doing.
Extending understanding of abilities in individuals and groups is an effective way of dealing with the Dunning-Kruger Effect. As to individuals, it can serve to vastly improve occupational and lifestyle outcomes. As to organizations, it can avert avert disasters and lost opportunities to improve the lot of the people, especially with respect to needs and protections.
Human abilities and the AI proposition
In the current environment, information technology (IT) has been held up as a means of improving human interactions. It is understood that digital networks, now more-or-less ubiquitous, help to deliver knowledge of a kind. Unfortunately, large-scale networks have now been found to provide incomplete solutions, even perverse and incorrect information. Just recently, for example, social media tools have been described as a threat to the public health (Ables, 2024).
Several kinds of AI tools are available in various forms. These are presented as a means of bridging the gap between knowledge as held among people and expressions of that knowledge via computers. There is a twist to such endeavors in the telling; technologists are adamant about retaining control of the digitization process. Based on my experience of over forty years, it is an article of faith among technologists.
Bill Gates, a successful entrepreneur who famously skipped college, is a major supporter of AI. Institutionally, Microsoft Corporation, which he founded, is the most dependable source of financing for OpenAI, the provider of Chat GPT, the most-mentioned AI offer currently. In a recent interview, he disclosed appalling weaknesses — particularly with respect to identifying knowledge.
Super intelligent AIs are in our future. Compared to a computer, our brains operate at a snail’s pace: An electrical signal in the brain moves at 1/100,000th the speed of the signal in a silicon chip! Once developers can generalize a learning algorithm and run it at the speed of a computer — an accomplishment that could be a decade away or a century away — we’ll have an incredibly powerful AGI (artificial general intelligence). It will be able to do everything that a human brain can, but without any practical limits on the size of its memory or the speed at which it operates. This will be a profound change (Gates and Melber, 2023).
These are strong statements by a technical specialist in advanced areas of cognitive science and educational philosophy. The hubris in this case is unfathomable. In one fell swoop, the knowledge product of millions of knowledge workers, including scientists, practitioners, bench lab workers, specialists, generalists, and peer reviewers is negated. It is somewhat refreshing to note some level of humility in the process, but it is much too little, much to late to redeem the AI enterprise.
Like everybody who’s involved with this, the way that it’s actually representing knowledge we don’t fully understand, we know how we trained it and made it guess and fill things in. We know how it figures out words and speech, but the fact that it’s so good, the exact specifics of where, how it’s storing, things we’re still researching… (Gates and Melber, 2023).
Once again, this is hubris to an unimaginable degree. It ignores the proposition that human knowledge exists. Furthermore, it represents a simplification if not nullification of knowledge-related achievements. This is an example of Dunning-Kruger by Mr. Gates and the other AI proponents on many dimensions. They see their tool and they are vaguely cognizant of risks, but they do not look further. They consider the AI effort as foundational — as an inevitability. It is not. Software improvement is warranted, but overcoming system shortfalls by throwing caution to the wind is hardly a useful approach.
Try out their AI tools — the language models and the image creators. In so doing, note whether you perceive that what they do is in any way in line with what a comprehensive view of human abilities coupled with a various means by which knowledge is arrived at is represented in them. It is possible that the best use of AI tools is to provide indexing and content manipulation tools to support their efforts at organizing and testing out knowledge-based processes. The part about subordinating human knowledge and action to their experiment is a non-starter.
It is important to consider the conditions under which knowledge products are to be created and by whom. If you consider that machines can think for you, you may still understand that they will not feel for you.
Instead, he said these consequences offer further reasons to continue developing advanced AI tools as well as regulations so that governments and corporations can detect, restrict and counter misuses using AI. “Cyber-criminals won’t stop making new tools… The effort to stop them needs to continue at the same pace,” he (Bill Gates) wrote (Shrivastava, 2023).
Let’s consider AI/machine prospects with the array of human abilities. As to cognition, they can calculate, sort, and distribute data very fast. We can use the speed, but judgment and sorting out difficult questions demand reflection and perception. As to language, the machines are sorting and organizing symbols. They are not reading, they are not understanding. As to cognition a four out of ten score is applied.
As the great musical composers took a great deal of time to write compositions that could then be performed in an instant, we would do well to use computers for a similar purpose.
Computers can gather data nicely, both symbols and numbers, but perception is not one of their strengths. A five out of ten is granted as to the direct sensory input capabilities of computers, omitting extrasensory input, which doesn’t apply.
There are a myriad of sensors available in the technical world. They can be used to gather information, but they are not sensory in the broader interpretive way felt by humans. All of the rules can be input as to desirability and undesirability and nuances of taste, smell, touch, and feeling, but these are all artificial and cannot bring primary benefit.
Affect is a big problem for machines. Affect-related abilities are fundamental to human decisionmaking. This accounts for zero out of ten as a score. Machines can do many physical activities via robotics and other means. These can serve both practical and recreational ends. This is a very different thing than to gain and to enjoy human growth and development by means of performances of many kinds, including athletic, musical, artistical. In this there is amusement and joy in participating and observing human activities. This can be supplemented by technologies, but they cannot be seen as getting in the way of human performance.
Similar factors relate to psychomotor as to physical phenomena. There are many advantages to turning to machines and other non-human devices and technologies in manufacturing, entertainment, transportation, and other functions. These shouldn’t be seen as competing with human achievements and forms of recreation. From control precision to speed in limb movement, computers register 100% marks, as they can be constructed and configured to do any such act. The score is thus ten out of ten.
Personality traits can be seen to program into machines and are rightly embedded in media. These may serve educational, training, entertainment, and other purposes, but once again make for incomplete and superficial interactions when compared to human abilities of this kind. Computers can be configured to feign personalities, but this does not serve to support real human personality elements. Thus, this scores a zero out of ten as well.
A chart of these scores can be seen below.
This chart shows strong and important benefits to be gained with more extensive computerization. Assuming that we can obtain knowledge products and other products of applied human abilities, the following combination analysis demonstrates how computing capacity rightly fits in to support human needs. They are of particular benefit due to their psychomotor, physical, and sensory capacities, which they can carry out using impressive speed, accuracy, and scale.
In order to consider whether AI outcomes are warranted, correct answers must be known. They must be arrived at quickly to deal with the rapid calculability of machine-based processing. The point is, as soon as dependable answers are available in digital forms.
But Gates’ claim that AI tools can be used to combat the deficiencies of other AI tools, may not practically hold up — at least not yet. For instance, while a range of AI detectors and deepfake detectors have launched, not all are always able to correctly flag synthetic or manipulated content. Some incorrectly portray real images as AI-generated, according to a New York Times report. But, generative AI, still a nascent technology, needs to be monitored and regulated by government agencies and companies to control its unintended effects on society, Gates said (Shrivastava, 2023).
A better way to deal with our ignorance problem is to bring people together and empower them in the digitization of their knowledge. This is a means of bringing together head, hand, and heart in the resolution of human challenges and problems. Following the pizza metaphor: More meat, less cheese.
References
Croissant, J. 2014. Agnotology: Ignorance and absence or towards a sociology of things that aren’t there. Social Epistemology, 28(1): 4–25.
Ables, K. 2024, January 26. New York City designates social media a public health hazard. Washington Post. https://www.washingtonpost.com/technology/2024/01/25/nyc-social-media-health-hazard-toxin/
Allen, D. K., (1983). DCLASS capabilities. Research Brief #2, CAM Software Laboratory. Provo, UT: Brigham Young University.
Allen, D. K., and Tingey, K. B. (2004). Taxonomy development: The super-science. Center for E-Commerce Seminar. Logan, UT: Utah State University College of Business. Available online: http://video.ncrtm.org/videoplayer.html?source=rtmp:/vod/mp4:Dell_K_Allen_2004.f4v&type=vod&idx=5.
Allen, D. K., and Alting, L. (1987). Manufacturing processes (Seventeen volume set of instructional materials for academic and industrial use by members of the Manufacturing Consortium). Provo, UT: Brigham Young University.
Allen, D. K, Tingey, K. B., Farnes, L., Millington, M. J. (in process). Comprehensive abilities model. Logan, UT: Utah State University.
Barabási, A. L., and Pósfai, M. 2016. Network science. Cambridge, UK: Cambridge University Press.
Bogdan, R. C., and Biklen, S. N. 1998/1992/1982. Characteristics of qualitative and quantitative research, 3rd ed. Boston: Allyn & Bacon.
Charmaz, K. 2006. Constructing grounded theory: A practical guide through qualitative analysis. London: Sage Publications.
Dougherty, E. R. 2009. Translational science: Epistemology and the investigative process. Current Genomics, 10, 102–109.
Employment and Training Administration. 2002a. O*NET ability profiler form 1: A tool for career exploration. Washington, DC: U. S. Department of Labor.
Employment and Training Administration. 2002b. O*NET ability profiler administration manual. Washington, DC: U. S. Department of Labor.
Farnes, L D. 2009. Physical performance quotient and qualifier (p2q2): Looking beyond the obvious in neuromusculoskeletal (nms) analysis. Tacoma, WA: Farnes Human Performance Awareness Institute.
Fleishman, E. A., and Quaintance, M. K. 1984. Taxonomies of human performance: The description of human tasks. Orlando, FL: Academic Press, Inc.
Fleishman, E. A., and Reilly, M. E. 1992/2008. Handbook of human abilities: Definitions, measurements, and job task requirements. Potomac, MD: Management Research Institute.
Gardner, H. 1983. Frames of mind: The theory of multiple intelligences. New York: Basic Books.
Gardner, H. 1999. Intelligence reframed: Multiple intelligences for the 21st Century. New York: Basic Books.
Gates, W. H. (Bill). 2023, March 21. The Age of AI has begun: Artificial intelligence is as revolutionary as mobile phones and the Internet. GatesNotes. https://www.gatesnotes.com/The-Age-of-AI-Has-Begun
Gates, W. H., and Melber, A. 2023, September 19. Bill Gates on Ari Melber.
MSNBC. https://www.youtube.com/watch?v=Ng5mYx4rDso
Glaser, B. G., and Strauss, A. L. 1967. The discovery of grounded theory: Strategies for qualitative research. New York: Aldine de Gruyter.
Guilford, J. P., 1967. The nature of human intelligence. New York: McGraw-Hill.
Korzybski, A. 1921. Manhood of humanity. New York: E. P. Dutton.
Kruger, J., and Dunning, D. 2009. Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Psychology, 2009, 1, 30–46. http://www.scirp.org/journal/psych).
Kurzweil, R. 2005. The singularity is near: When humans transcend biology. New York: Penguin Books.
Looti, M. 2022, November 19. Guilford’s theory of intelligence. Psychological Scales. https://scales.arabpsychology.com/2022/11/19/guilfords-theory-of-intelligence/
Murh, T. 2004. User’s manual for Atlas.ti 5.0. Berlin: Atlast.ti Scientific Software Development GmbH.
Paper, D. J., and Tingey, K. B. 2002. Application of tree-based solutions: A case study with INEEL. Annals of Cases on Information Technology, 4, 260–271.
Parsons, T. 1937. The structure of social action. New York: McGraw-Hill, 1937.
Scherkenbach, W. W. 1991. Deming’s road to continual improvement. Knoxville, TN: SPC Press.
Schlichter, C. L., and Palmer, W. R. 1993. Thinking smart: A primer of the Talents Unlimited model. Mansfield Center, CT: Creative Learning Press, Inc.
Shrivastava, R. 2023, July 12. Why Bill Gates isn’t worried about AI models making stuff up. Forbes. https://www.forbes.com/sites/rashishrivastava/2023/07/12/why-bill-gates-isnt-worried-about-ai-models-making-stuff-up/?sh=7961a72f40e7
Sternberg, R. J. 1988. The triarchic mind: A theory of human intelligence. New York: Viking.
Sternberg, R. J. 2000a. Patterns of giftedness: A triarchic analysis. Roeper Review, 22(4), 231–240.
Sternberg, R. J. 2000b. The concept of intelligence, in R. J. Sternberg (Ed.), (2000) Handbook of intelligence. Cambridge, UK: Cambridge University Press.
Sternberg, R. J.. 2003. The Rainbow Project: Enhancing the SAT through assessments of analytical, practical, and creative skills. New Haven, CT: Yale University. The College Board, The Rainbow Project. Unpublished manuscript.
Tingey, K. B. 2008. Model for information systems legitimacy in rehabilitation counseling. Saarbrucken, Germany: VDM Verlag Dr. Mller.
Tingey, K. B. 2009/2018. Methods-based management: Breakthrough performance on leaner budgets. San Diego, CA: University Readers.
Tingey, K. B. 2014. The solution: Permanescence. Logan, UT: Profundities LLC.
Tingey, K. B., and Kundu, M. 2010. Universal design of clinical and translational science for improved human service outcomes. NCRE 2010 Spring Conference. Manhattan Beach, CA: National Council on Rehabilitation Education (NCRE).
Todd, R. H., Allen, D. K., and Alting, L. 1994. Fundamental principles of manufacturing processes. New York: Industrial Press Inc.
Todd, R. H., Allen, D. K., and Alting, L. 1994. Manufacturing processes reference guide. New York: Industrial Press Inc.
Wasserman, S., and Faust, K. 1992. Social network analysis. Cambridge, UK: Cambridge University Press.
Whitehead, A. N., and D. R. Griffin and D. W. Sherburne, (Eds.). 1929/1979. Process and reality: An essay in cosmology (1929). 1979 corrected edition, edited by Free Press.
Wikipedia contributors, “Legal opinion,” Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=Legal_opinion&oldid=1186131993 (accessed December 1, 2023).
World Health Organization. 2001. International Classification of Functioning, Disability, and Health: ICF short version. Geneva: World Health Organization.
World Health Organization 2003. ICF checklist version 2.1a, clinician form for International Classification of Functioning, Disability, and Health. Geneva: World Health Organization.