C-44. K hardness
We have a well-established scale of hardness for rocks, extending from very soft (steatite: H = 1) to very hard (diamond: H = 10). When we set out to use rocks for doing something we find such hardness knowledge very helpful. We have a similar need, but not so good a measure, for observational products – i.e., for acquaintances (aka knowledges) of this or that sort.
No scale at all, actually. There is an obvious difference between a passing, “nodding” acquaintance and the “scientific method” (however each is specified), but nothing so helpful as a 1 to 10 rating for what we hear or read or see — as on the Internet. Surely this is a grave fault if we deem ourselves members of an “Information Society.”
And what’s worse is that a hardness scale for knowledges would have to range from minus 10 to plus 10. We speak of truth and falsity. We discuss the reliability of observations in terms of their functionality and dysfunctionality. Some of what circulates as knowledge is incredible. Some of this has been that way for centuries. (It does, however, make the need for minding capability – and a developed editing functionality — very credible.) But in any case it is commonplace that minding’s products have been, and still are, circulating freely with inadequate grading – and even what grading we have is of little effect (e.g., the discrepancy between scientific evidence and public belief).
Learning as a process, we have said (IX, XI), can be distinguished from the process of knowing – this even though popular conceptual usage equates their products (learning = knowledge). But if learning is seen as acquiring knowledge (or learning products) already possessed by someone else, then K-hardness becomes of paramount concern. Efficient instructional strategies, though still of great importance, have to take a second seat to the consequentiality of knowing processes and their products on which learning’s value and the value of anything learned are contingent. (See C-31 for the crucial, in light of consequentiality as a test, distinction between evaluation and valuation.)
The “scientific method” extolls the virtue of consequentiality as a test of knowledge – and not incidentally of the virtue of a certain group of professional observers. Occasions for employing consequentiality in this classic experimental manner are limited. Scientists, self-designated or not, develop their acquaintances in many ways, and their observations may have a limited, even negative, K-hardness. The near-insistent application by many of BPO-biased methods offers evidence of that (C-38, C-39).
Consequentiality can serve the cause of K-hardness in an experimentally different way. “Trial and error” is its most primitive form. The consequentiality test is apparent even there. It’s the “trial” which needs further development, so that the outcome can be more telling. Our analysis of compositional change (II) initiates an attempt to point the way ahead. Process consequentiality (C-16) carries this forward, bringing the pragmatism of cognized step-making and taking outcomes to bear on each and any compositional contributor. This is developmental. This is the sense in which experience can be a better teacher (C-42).
The lack of K-hardness is bad enough. Learning can make it worse if it magnifies the frequency in which a given poor K appears. Any K may have a long tail of learning, with the end of the tail often having a different tale to tell (as rumor studies inform us).
As observational content piles up, acquaintances being exchanged as on the Internet, learned items tend to accumulate faster than new knowledges. Thus, though the Internet’s cyberspace provides embodied accommodation for a near-infinity of observations, the problem of K-hardness continues – now made more difficult because the growing ball of strung-out learned items rivals conceptual abstraction in its remoteness from improved acquaintance, in its lack of grasp (C-32).
Crystal structure in consequence of heat and pressure can help produce hardness in rocks (e.g., the familiar story of graphite and diamonds). Singularity, whether in service of directed behavior (VIII) or for an observer’s desired order of things (III), provides pressure for our constructing knowledge structures. Knowledge structures have the potential to improve the crystal quality, and the hardness, of the particular observations they comprise. However, depending on how that structure is obtained, the crystal may be flawed functionally. (For example: Hard, but brittle? Or: Hard, but relatively rare?) The support which each crystal’s structural element lends to its companions is welcome, not least to the observations’ provisioners.
Knowledge structures vary in compositional elements and relations. Cultures, for example, comprise an accumulation of behavioral solutions (I: Sbeh) as elements, many of which have become normative principles for a community (“how we do things here”) A logically coherent theory, such as in the hypothetico-deductive mode (V), is notable for its relations not its (perhaps content-free) elements (X). Stories – myths, sagas, bibles, survival accounts … narratives of many kinds each comprise knowledges various in their quantity and quality.* Political constitutions do the same for known values, and may or may not yield to subsequent evaluation needs or results.
We may also assess K structures for their hardness in terms of how much consequentiality has been introduced into the acquaintance process. Images and thus visual memory might seem K-hard because of their fidelity. But they miss the consequentiality of impressions and their verbal and emotive memories which, though relatively soft, have the advantage of an evaluative process genesis. Outcomes are persuasive.
Unfortunately, too many minted knowledge structures (e.g., “models,” “theories”) seem obese, overly influenced by summary value (see C-19). But as paradigm shifts and the dinosaurs tell us, size is no prohibitor to a fall.
How do we improve K-hardness? By inquiry, clearly. Improved questioning capability, especially as applied in problem solving, improves the quality of evaluation’s employment of consequentiality. And we improve K-hardness by investing more in the process of knowing relative to the process of learning, as in our educational efforts. Learning per se can be a “down the garden path” experience, even to the forfeit of a life of productive problem solving to one of “consumer” decision making (XI).
(* Languages are knowledge structures, sometimes very biased forms of acquaintance as exemplified by the BPO bias [C-39]. As discussed with regard to the investment we still need to make in language development [in C-48], languages are very material – as in “material sciences,” with noteworthy characteristics that may or may not improve the K-hardness of linguistic representations.)
(c) 2012 R. F. Carter
No scale at all, actually. There is an obvious difference between a passing, “nodding” acquaintance and the “scientific method” (however each is specified), but nothing so helpful as a 1 to 10 rating for what we hear or read or see — as on the Internet. Surely this is a grave fault if we deem ourselves members of an “Information Society.”
And what’s worse is that a hardness scale for knowledges would have to range from minus 10 to plus 10. We speak of truth and falsity. We discuss the reliability of observations in terms of their functionality and dysfunctionality. Some of what circulates as knowledge is incredible. Some of this has been that way for centuries. (It does, however, make the need for minding capability – and a developed editing functionality — very credible.) But in any case it is commonplace that minding’s products have been, and still are, circulating freely with inadequate grading – and even what grading we have is of little effect (e.g., the discrepancy between scientific evidence and public belief).
Learning as a process, we have said (IX, XI), can be distinguished from the process of knowing – this even though popular conceptual usage equates their products (learning = knowledge). But if learning is seen as acquiring knowledge (or learning products) already possessed by someone else, then K-hardness becomes of paramount concern. Efficient instructional strategies, though still of great importance, have to take a second seat to the consequentiality of knowing processes and their products on which learning’s value and the value of anything learned are contingent. (See C-31 for the crucial, in light of consequentiality as a test, distinction between evaluation and valuation.)
The “scientific method” extolls the virtue of consequentiality as a test of knowledge – and not incidentally of the virtue of a certain group of professional observers. Occasions for employing consequentiality in this classic experimental manner are limited. Scientists, self-designated or not, develop their acquaintances in many ways, and their observations may have a limited, even negative, K-hardness. The near-insistent application by many of BPO-biased methods offers evidence of that (C-38, C-39).
Consequentiality can serve the cause of K-hardness in an experimentally different way. “Trial and error” is its most primitive form. The consequentiality test is apparent even there. It’s the “trial” which needs further development, so that the outcome can be more telling. Our analysis of compositional change (II) initiates an attempt to point the way ahead. Process consequentiality (C-16) carries this forward, bringing the pragmatism of cognized step-making and taking outcomes to bear on each and any compositional contributor. This is developmental. This is the sense in which experience can be a better teacher (C-42).
The lack of K-hardness is bad enough. Learning can make it worse if it magnifies the frequency in which a given poor K appears. Any K may have a long tail of learning, with the end of the tail often having a different tale to tell (as rumor studies inform us).
As observational content piles up, acquaintances being exchanged as on the Internet, learned items tend to accumulate faster than new knowledges. Thus, though the Internet’s cyberspace provides embodied accommodation for a near-infinity of observations, the problem of K-hardness continues – now made more difficult because the growing ball of strung-out learned items rivals conceptual abstraction in its remoteness from improved acquaintance, in its lack of grasp (C-32).
Crystal structure in consequence of heat and pressure can help produce hardness in rocks (e.g., the familiar story of graphite and diamonds). Singularity, whether in service of directed behavior (VIII) or for an observer’s desired order of things (III), provides pressure for our constructing knowledge structures. Knowledge structures have the potential to improve the crystal quality, and the hardness, of the particular observations they comprise. However, depending on how that structure is obtained, the crystal may be flawed functionally. (For example: Hard, but brittle? Or: Hard, but relatively rare?) The support which each crystal’s structural element lends to its companions is welcome, not least to the observations’ provisioners.
Knowledge structures vary in compositional elements and relations. Cultures, for example, comprise an accumulation of behavioral solutions (I: Sbeh) as elements, many of which have become normative principles for a community (“how we do things here”) A logically coherent theory, such as in the hypothetico-deductive mode (V), is notable for its relations not its (perhaps content-free) elements (X). Stories – myths, sagas, bibles, survival accounts … narratives of many kinds each comprise knowledges various in their quantity and quality.* Political constitutions do the same for known values, and may or may not yield to subsequent evaluation needs or results.
We may also assess K structures for their hardness in terms of how much consequentiality has been introduced into the acquaintance process. Images and thus visual memory might seem K-hard because of their fidelity. But they miss the consequentiality of impressions and their verbal and emotive memories which, though relatively soft, have the advantage of an evaluative process genesis. Outcomes are persuasive.
Unfortunately, too many minted knowledge structures (e.g., “models,” “theories”) seem obese, overly influenced by summary value (see C-19). But as paradigm shifts and the dinosaurs tell us, size is no prohibitor to a fall.
How do we improve K-hardness? By inquiry, clearly. Improved questioning capability, especially as applied in problem solving, improves the quality of evaluation’s employment of consequentiality. And we improve K-hardness by investing more in the process of knowing relative to the process of learning, as in our educational efforts. Learning per se can be a “down the garden path” experience, even to the forfeit of a life of productive problem solving to one of “consumer” decision making (XI).
(* Languages are knowledge structures, sometimes very biased forms of acquaintance as exemplified by the BPO bias [C-39]. As discussed with regard to the investment we still need to make in language development [in C-48], languages are very material – as in “material sciences,” with noteworthy characteristics that may or may not improve the K-hardness of linguistic representations.)
(c) 2012 R. F. Carter
S