Binary jargon: The metaphoric language of computing, C Van Dyke

Tags: metaphor, computer science, computer scientists, metaphors, Technical terms, Neologisms, computer sci ence, specialized terminology, Renaissance, familiar, technical terminology, Computer illiteracy, Computing Carolynn Van Dyke Lafayette College, Computing, Peter Laurie, political science, Peter De Bono, intimate relations, Unfamiliar words, John A. Barry, Francis W, literally means, George Whalley, cursor movement, computer professional, science, computer professionals, Marvin Minsky, Operations research, Fraunhofer diffraction, Normal profit, Hugh D. Young, Personal tax, Mark W. Zemansky, Overhead costs, Richard Kittredge
Content: Binary Jargon: The Metaphoric Language of Computing Carolynn Van Dyke Lafayette College
The greatest thing by far is to be a master of metaphor. It is the one thing that cannot be learnt from others; and it is also a sign of genius, since a good metaphor implies an intui tive perception of the similar ity in dissimilarities. --Aristotle, ca. 330 B.C. [2, 1458b] The early Fathers could not pro perly represent some things by the images of others unless trained, as I have said, in the hidden alliances and affinities of all nature. --Pico della Mirandola, 1489 [6 , p. 79] For in many w a y s , the modern theory of computation is the long-awaited science of the relations between parts and wholes. . . . Computer Science has such intimate relations with so many other subjects that it is hard to see it as a thing in itself. --Marvin Minsky, 1979 [4, p. 393] An administrator at my institution was recently advised to talk with the head of data processing. "I can't; she doesn't speak English," replied the administrator, to a chorus of nods and grins from eaves dropping colleagues. English is in fact the data proces sing supervisor's native language, but she is by no means the first computer profes sional to be accused of incomprehensi bility. Students complain that computer instructors cannot communicate; columnists call the language of computer scientists "computer illiteracy"; satirists represent "CompuSpeak" with invented monstrosities such as "user-friendly liveware" (sales person) and "anthroperipheral interface error" (human error). One introductory textbook compares computer terminology
with the Latin of medieval scholars--an impractical, elitist tongue unintelligible to ordinary people.<<1>> In self-defense, computer profession als may point out that any technical field needs technical terminology and that their own jargon is unusually user-friendly [7]. The defense is valid, but it fails to con vince our accusers--and for good reason. The technical terminology of computing differs from that of other fields in a way that makes it more difficult even while appearing simpler. Computing has given us a new and paradoxical kind of jargon, based on an apparently unscientific, non technical principle: metaphor. Anatomy of a glossary Like languages in general, the "sub languages" of special areas can be created in various ways. First, new terms can be introduced, usually from foreign roots, affixes, or proper names. For instance, plant biologists appropriated "Aufwuchs," a German term for "luxuriant growth," to designate organisms that coat submerged materials; ecologists combined two Greek affixes to produce "xerophile," or drought-loving; physicists named a measure of engine efficiency the "Carnot cycle" after its formulator. Second, existing but uncommon words may be appropriated and perhaps combined in a new phrase, such as "inert gas" (chemistry), "differential equation" (mathematics), "revolution of rising expectations" (Political Science),
<>"Computer illiteracy" is dis
cussed by John A. Barry, New York T i m e s ,
February 19, 1984, p. A 3 0 . "User-friendly
liveware" comes from a Doonesbury cartoon.
"Anthroperipheral interface error" was the
subject of an article in the October 1983
issue of P r o , a magazine for Kaypro users.
The comparison with medieval Latin is made
by Robin Bradbeer, Peter De Bono, and
Peter Laurie, The B e g i n n e r 's Guide to Com
puters (Reading, MA:
1982), p. 31.
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or "theater of the absurd" (literature). Where the first method produces obviously specialized language, inscrutable for the outsider, the second yields terms that are somewhat more familiar but still in need of explanation. A third method is to use words familiar from other contexts but to give them new meanings, as the psychoana lyst did with "working through," the phy sicist with "charmed," and the geologist with "fault." A term formed in this third way needs explanation, like the products of the second method, but unlike them, it does not seem in itself to be part of a technical vocabulary. The three methods result in three varieties of jargon, which may be called neologisms, technical terms, and redef initions. Table 1 summarizes the cate gories and provides examples from computer science. All academic fields probably use all three categories, but not in the same proportions. We associate exotic neolo gisms particularly with the natural sci ences, for instance, and mouthfilling "technical terms" with the social sci ences. What of computer science? Does it share with physics a fondness for neolo gisms, or does it rely like economics pri marily on abstract words and phrases? To answer that question, I compared the vocabularies of six introductory text books: two from computer science (a pro gramming text and an introduction to information processing), two from the natural sciences (ecology and physics), and two from the social sciences (econ omics and psychology). From the glos saries of those books I chose terms at an arbitrary fixed interval; I then assigned the terms to the categories in Table 1. Table 2 summarizes the results, using the three general categories described in the previous paragraph. As expected, the natural science textbooks gloss mostly neologisms, and the social science books mostly technical abstractions. In con trast, the programming and informationprocessing texts rely heavily on "redef initions," employing a far higher propor tion of such terms than do any of the other books.
Second-hand jargon
Redefined terms possess a split per
On the one hand, the terms
themselves are familiar. Of the forty
redefined terms in the sample from the
programming glossary, as listed in the
Appendix, at least thirty-five have long
been in general use: argument, b l o c k ,
comment, constant, device, directive,
d r i v e r , evaluate, external, f i e l d , and so
forth. Hearing them, the outsider may
feel at home.
On the other hand, within computer science these long-familiar terms have taken on new identities, which usually cannot be deduced from the old ones. The common meanings of "argument," for exam ple, do not reveal what the term means to a programmer: specific values used in generic commands. Someone who knows a "block" only in its ordinary sense, as a visible unit or segment, cannot identify a block of records or of program statements. "Be sure to comment your code adequately" makes little sense unless one knows the relationship of program comments to source code, compilers, and executable state ments. Then again, computer science makes peculiar use of sour c e , code, compile, e x e cutable, and statement. In short, the newcomer is likely to feel not so much that computer scientists speak a different language as that they have rewritten the dictionary. Among students of language, the application of "a word which in ordinary usage signifies one kind of thing, quality, or action . . . to another, with out express indication of a relation between them" [1], has long been known as the use of metaphor. Literary scholar W. B. Stanford describes metaphor as follows: "A term (X) normally signifying an object or concept (A) [is used] in such a context that it must refer to another object or concept (B) which is distinct enough in characteristics from A to ensure that in the composite idea formed by the syntheses of the concepts A and B and now symbolized in the word X, the factors A and B retain their conceptual independence even while they merge in the unity symbolized by X" [8, p. 101]. Stanford's elaborate formulation can be applied precisely to the redefined terms of computer science. For instance, block (Stanford's "word X"), normally sig nifying a spatial unit or group of objects ("concept A"), is applied to a collection of program statements related both phy sically and syntactically ("concept B"). Run, which traditionally denotes rapid and unrestricted physical action, comes to mean also the automatic and--one hopes-unimpeded execution of commands. It must be admitted that the two meanings of a computing term may not "retain their conceptual independence" for all speakers. That is, the computer pro fessional uses block, connector, field, window, and document without remembering what those terms meant B.C. (Before Com puters). Concept B has swallowed Concept A: the metaphors are dead. Meanwhile, they have not yet come to life for the novice, who waits in vain for Concept B to emerge from the pre-computer Concept A. But for anyone who understands their full range of meaning, such terms are, in origin and structure, metaphors.
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CATEGORY Neologisms Technical terms Redefinitions
EXPLANATION New words formed from foreign roots or affixes or proper names unfamiliar words and new phrases Familiar terms or com binations used with new meanings
EXAMPLES FROM COMPUTER SCIENCE algorithm, cybernetics, octal, Boolean magnetic core, flowchart, sequential control block, field, pack, string
Table 1: Categories of specialized terminology, according to method of derivation.
The inevitability of metaphor That computer scientists should model their vocabulary on that of poetry may indicate that they possess overactive imaginations--a suggestion that some of their colleagues would readily confirm. Alternatively, their penchant for metaphor may reveal their ignorance of the foreign terms that could generate neologisms. More charitably, we could infer that they use metaphors because they wish to lead nonspecialists into their territory gen tly, by way of familiar concepts. On the other hand, Richard Kittredge, a specialist in technical sublanguages, argues that the characteristics of a sub language reflect not simply the idiosyn crasies of its users but the structure of knowledge in the field itself [3]. If he is right, metaphor may be in some way par ticularly appropriate to computer science.
A review of the definition of meta phor bears out Kittredge's generalization. Metaphors, we are told, partially identify concepts that are normally regarded as distinct. We might expect therefore that they will arise when links are being forged or perceived between diverse ob jects or ideas. For instance, a sublan guage will be metaphoric if its speakers habitually find parallels between their field and others.
And indeed, computer scientists spend
much of their time seeking and extending
such parallels.
Their work consists
largely of studying, creating, and con
trolling electronic systems that imitate
other systems: logic diagrams, people do
ing arithmetic, chess-players, children
playing with blocks, ledgers and files,
the flow of traffic through an intersec
tion, the behavior of a nation's economy
over a certain period of time. That is.
Information Programming Psychology Economics Physics Ecology
Technical terms
6.9% 2.8% 15.3% 4.6% 19.6% 94.1%
31.0% 40.3% 71.5% 82.8% 65.2% 5.9%
Redefini tions 62.1% 56.9% 11.1% 5.7% 7.6% 0%
Table 2: Comparison of glossaries in six textbooks. (Categories are from Table 1.)
NOTE: For Psychology, Physics, and Economics the three percentages are short of 100% because a few glossary entries did not fall into any of the three categories. The Appendix to this article lists all of the terms by category and provides addi tional details about the survey.
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computer scientists build simulations and models.
Historians tell us that the philoso phers and poets of the Renaissance used metaphor to expound detailed correspon dences among the universe, the "body poli tic," and the human microcosm [9]. In the words of one Renaissance philosopher, "Versed in all things and inspired by that Spirit which not only knows all these things but made them, they aptly symbo lized the natures of one world by those which they knew corresponded to them in the other worlds" [6, p. 79].<<2>> So too, perhaps, the creators of computa tional microworlds use metaphor because they see reality through the lens of analogy.
Indeed, computer scientists can be
expected to cling even more tenaciously to
metaphor than did the analogy-makers of
the Renaissance, for their microworlds are
inherently symbolic. When a Shakespearean
character calls the Roman Senate the belly
of the State or brands a politician "a
disease that must be cut away," he iden
tifies the human body with the nation, but
in such a way that they can still be
clearly distinguished. Separate terms for
Concepts A and B are still available. But
the objects and processes of computing can
scarcely be named apart from the elemen
tary objects and processes that they
Granted, we can call records
"identical sequences of variously struc
tured memory locations," or windows "buf
fered and overlapping screen displays."
But such circumlocutions are not only ver
bose but imprecise. The commands that set
up the electronic data-structure called a
"record" were intended to reproduce the
structure of office records. Similarly,
the meaning of a screen-display window
lies less in techniques than in the effect
the techniques produce--the opening of a
window into another realm.
Thus the computer is a symbol-manipu lator, as Joseph Weizenbaum says, not only because it can represent virtually any thing, but because it is designed to represent other things--to represent, in fact, many of the fundamental processes and relations of physical and mental reality [ll, p. 74]. According to the literary critic A. D. Nuttall, "There appear to be certain areas of discourse where we can never afford to give up the metaphors we have inherited. Perhaps the principal examples of this sort of area are, first, theology, and, second, lan guage about the mind. . . . Your metaphy-
<<2>>Pico is referring to the early patristic writers, but the practices he describes were the goal also of his con temporaries.
sician is a great metaphorist" [5, p. 22]. So, I would add, is your computer scien tist. The vocabulary of computer science is incurably metaphoric because its sub ject matter is paradigmatic.
Living with metaphors
Whatever their metaphysical pedigree, computer metaphors are surprisingly troublesome in everyday communication. For the layperson they can be semantic wolves in sheep's clothing, more dangerous than terminology that openly announces its strangeness. Instructors are less likely to define "field" or "access" than "aufwuchs" or "revolution of rising expecta tions," and students are less likely to demand explanations. Moreover, when a familiar-sounding term is defined, stu dents may not grasp the definition, for past associations obstruct new ones. Long familiar with "drive" and "argue," the programming student may not even hear the instructor's explanation of device drivers or the arguments of functions.
The split between familiar and tech
nical meanings has its comic side. As
cartoonists know, it generates puns: one
monk points to another who is busy with a
microcomputer and says, "This is our chip-
But for the would-be computer
user, ambiguous jargon is less funny.
Recently I overheard a young man explain
ing to a middle-aged librarian how to
remove the disk from a Macintosh. "Well,
first you go to where it says 'file,'" he
began, only to be interrupted with, "Now,
in what sense are you using the word
'go'?" The question might have been amus
ing if the librarian's hands and voice had
not been trembling with anxiety.
Both parties to such a miscommunication are likely to believe that they are using words in their proper or literal senses. For the computer professional, cursor movement is indeed movement, and data fields are areas? thus the uses of "go" and "field" in their new context are transparently appropriate. The new user will retort that "go" means "to move phy sically" and "field" literally means "an area of ground or study." That is the way of metaphors: depending on one's frame of reference, a metaphor's "literal" meaning may be either its older meaning or its extended one.
We might conclude that computer sci ence should renounce metaphor altogether in favor of more direct terminology. But it is difficult to imagine a concise, nonmetaphoric alternative to "go" or "field." I have argued in fact that meta phors are indigenous to computer science.
If we cannot banish computer meta phors, we can nonetheless try to tame
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them--to render them less confusing. To
begin with, we can acknowledge that they
are indeed metaphors. In everyday lan
guage, unrecognized metaphors pose few
problems because they are unrecognized
No one now remembers--or
cares--that "interval" once meant "the
space between two pillars," or that
"daisy" comes from "day's eye." But in a
new and changing area such as computer
science, metaphors that some speakers no
longer recognize may be all too fresh for
others. The professional who treats "go"
as flatly equivalent to "move the cursor"
and the novice for whom "go" simply means
"move one's body" are stranded on either
side of the difference between "concept A"
and "concept B." Novices cannot bridge
the gap, for they can neither forget Con
cept A nor pass by an act of will to Con
cept B. It is computer professionals who
must build the bridges, and we can do so
only if we can see the distance to be
bridged. That is, we must remember and
acknowledge the original meanings of our
metaphoric terms.
In addition, we can make our meta phors' new meanings concrete. Although computer metaphors refer ultimately to abstract categories of space and thought, most of them also designate specific pro cedures or objects in computing. It is those procedures and objects--the meta phors' new context--that the novice finds baffling.
To elucidate the context, we can use several techniques. One, which many in structors use already, is to display exam ples: sample records and fields, typical arguments, and so forth. Another is to make a term operational, associating it with particular actions: the keystrokes or mouse-movements that correspond to "go," the words or punctuation that deli mit a "block." Finally, we can map a term's new context by mentioning its oppo sites. We can point out, for instance, that in the world of computing "run" con trasts with "load" or "program," "imple ment" is the opposite of "plan," "window" stands opposed to "the whole screen," and "go" is an alternative to "give a com mand." With such guidance, the novice can enter the metaphor's new semantic realm, look back at the pre-computer meaning, and grasp what Pico della Mirandola would call the "hidden alliances and affinities" between them.
The foregoing discussion may imply
that only teachers and new users need to
see both dimensions of computer meta
phors--that specialists, who work only
within Concept B, can safely pull up the
bridge that carried them there.
indeed, professionals can understand each
other well enough without remembering the
semantic depth of their jargon.
On the other hand, a sensitivity to
metaphor may benefit research and analysis
in computer science itself. It may have
been the metaphor in go that inspired a
new kinesthetic device for cursor-move-
m e n t --the m o u s e . In the same vein, the
architectural metaphor underlying window
could suggest other convenient interfaces
between program and user:
what about
doors, or curtains? To take a more the
oretical example, anyone seeking to under
stand structured programming can benefit
from a study of the relationships among
various kinds of "block," in and out of
computer science.
More broadly yet,
unlimited challenges appear to those who
take seriously the great metaphor implicit
in such terms as intelligence, m e m o r y , and
even computer (originally "person who com
putes"). The identity that such terms po
sit between a human Concept A and a mecha
nical Concept B raises some of the central
and enduring questions of computer sci ence.
Metaphor, writes George Whalley, may
"prove to be the radical mode in which we
correlate all our knowledge and experi
ence" [10].
If computer science is
indeed, in Marvin Minsky's words, "the
long-awaited science of the relations
between parts and wholes" [4, p. 393], its
achievements depend in part on the vital
ity of the metaphoric imagination.
1. Abrams, M. H. Figurative Language. In A Glossary of Literary T e r m s , Holt, Rinehart and Winston, New York, 1965.
2. Aristotle. P o e t i c s , trans. Ingram Bywater. In The Pocket A ristotle, J. D. Kaplan, Ed. Washington Square Press, New York, 1958.
3. Kittredge, R. Variation and Homo
geneity of Sublanguages. In Sublan
Studies of Language in
Restricted Semantic Do m a i n s . R.
Kittredge and J. Lehrberger, Eds.
Walter de Gruyter, Berlin and New
York, 1982. Pp. 107-37.
4. Minsky, M. Computer Science and the
Representation of Knowledge. In The
Computer Age: A Twenty-Year V i e w , M.
L. Dertouzos and J. Moses, Eds. MIT
Press, Cambridge, 1979.
5. Nuttall, A. D. Two Kinds of Alle gory: A Study of Shakespeare's "The Tempest" and the Logic of Allego rical Expression. Routledge & Kegan Paul, London, 1967.
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6. Pico della Mirandola.
trans. Douglas Carmichael. In "On
the Dignity of Man" and Other W o r k s .
Bobbs-Merrill, Indianapolis, 1965.
7. Simmons, L. Computer Talk: UserFriendly Jargon. Technology Review, November/December 1985, p. 83.
8. Stanford, W. B. Greek M e t aphor. Blackwell, Oxford, 1936.
9. Tillyard, E. M. W. The Elizabethan World Picture. Chatto & Windus, London, 1943. Chapter 7.
10. Whalley, G. Metaphor. In Princeton Encyclopedia of Poetry and Poe ti cs . Princeton University Press, Prince ton, 1974.
11. Weizenbaum, J. Computer Power and Human R e a s o n . W. H. Freeman, San Francisco, 1976.
Appendix: The Survey of Textbooks. Below is bibliographic information on the six textbooks that I surveyed, fol lowed by the terms that I selected from their glossaries. The terms are grouped according to the categories described in the body of this essay. In most cases I selected every fourth term from the glossary, beginning with a random selection from the first four. Be cause I could not find a physics text with a glossary, I selected a bold-faced, de fined term from every tenth page of the physics text, moving on to the next or previous page if the tenth page contained no bold-faced and defined terms, and al ternating between the first and last word on the selected page. Terms were assigned to categories primarily by virtue of current percep tions, not original meanings. Some that might be considered "general terms" are listed instead as "technical terms" be cause it seemed to me that they are now heavily associated with technical sub jects. For instance, program (from the programming text) probably functioned at one time as a general term, and hence a metaphor, for most nonspecialists, but it is now so firmly identified with computer science that most speakers regard it as part of that field's technical vocabulary. Probably more terms will undergo the same change in the future. I predict, however, that in computer science, much more than in other fields, new "general terms" will be ferried in, as metaphors, even as older borrowings become naturalized into the technical vocabulary.
Acronyms and initialisms are included among "neologisms" because like genuine neologisms, they are semantically opaque to the outsider.
Some terms from the economics and physics texts are listed here as "ot h e r " because they not fall into any of the three categories. They might be placed into a fourth category: "ordinary words
made more precise." Like a "redefini
tion," the physicist's wave and the econo
mist's labor are familiar from other con
Unlike redefinitions, however,
such terms are not redefined by the tech
nical field; instead, their definitions
are quantified or operationalized. They
present semantic problems of a different
sort than those posed by redefinitions.
Information-processing text. Schmidt, Richard N., and William E. Meyers. Introduction to Computer Science and Data Processing. 2nd ed. New York: Holt, Rinehart and Winston, Inc., 1970. Neologisms. ADP; (Automatic Data Processing), Algorithm, Cybernetics, Millisecond, Multiplex Technical terms. Binary, Closed subroutine, Data organization. Equipment, Peripheral, Flowchart, Linear programming, Magnetic core, Magnetic ink, mathematical model, Numerical analysis, Octal, Problem description, Program, Rounding, Sequential control, Software, Terminal General terms. And, Array, Attribute, Block, Byte, Central processing unit, Collate, Compile, Connector, Decision table, Document, Edit, Field, Fixed point, Gang punch, Head, Index, Input, Item, Key, Loop, Message, Operating system, Output, Pack, Patch, Real t i m e , Relocate, Run, Storage, String, Switch, Table look up, Transfer, Unconditional, Unit record, Verification Programming text. Cooper, Doug, and Michael Clancy. Oh! P a s c a l ! 2nd ed. New York: W. W. Norton, 1985. Neologisms. Get(f), Paren Technical terms. Assignment operator, Cardinality, Compile-time error, Compound statement, Correct, Defensive programming, Efficiency, Empty statement, Evaluate, Exit condition, File window, Function, Iteration, List disposal, Mnemonic,
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Operand, Predefined identifier, Program heading, Pseudorandom, Scale factor, Selection sort, Set operator, Solution space, Standard input, Standard output, State variable, Subrange, System defined, Top-down debugging, User-defined ordinal type General terms. Actual (parameter), Allocate, Argument, Batch (computer or program), Bottom-up, Bubble s o r t , Comment, Constant, Curly brackets, Device, Directive, Driver, External, Field list, Flakey, Global, Handwave, Identifier, In, Indirect a ccess, Input, Library, Location, Massage, Nesting, Number crunching, Output, Pop, Queue, Real w o r l d , Reference, Representation, Right thing, Root, Square brackets, Stepwise refinement, Structured walkthrough, Terminated, Transparency, Truth table, Undefined Psychology text. Atkinson, Rita L . , Richard A. Atkinson, and Ernest R. Hilgard. Introduction to Psychology. 8th ed. San Diego: Harcourt Brace, 1983 . Neologisms. Androgens, Antidiuretic hormone, Cannon-Bard theory, Cochlea, Dendrite, Dichromatism, Endocrine gland, GSR (galvanic skin response), Gonads, Id, Interneurons, Introvert, Kinesthesis, LSD (lysergic acid derivatives), Mantra, Morpheme, Myelin sheath, PS4R method, Phenomenology, REMs (rapid eye movements), Saccade, Superego, Synapse, Volley principle Technical terms. Achievement, Adrenalin, Age regression, Ambivalence, Anxiety hierarchy, Assertive training, Basal mental age, Behavior therapy, Biofeedback, Brain s t e m , Central core (of brain), Cerebral hemispheres, Chronological age (CA), Classical conditioning, cognitive dissonance, Color blindness, Complex cell, Conditioned stimulus (CS), Conscience, Counter conditioning, Cumulative curve, Defense mechanism, Depolarization, Displaced aggression, Dominant gene, Eardrum, Ego, Electroshock therapy, Equilabratory senses, Evoked potential, Experimental method, factor analysis, Fixation, Free association, Frustration-aggression hypothesis, Gene, Genetics, Hedonism, Heterosexuality, Hypnotic induction, Illusion, Independent variable, Inner ear, Intellectualization, Lateral fissure, Light adaptation, Localized functions, Memory span, Mental imagery, Middle ear, Narcotics, Negative reinforcement, Neurosis, Norm, Obsessive-compulsive disorder,
operant behavior, Oral stage, Ovarian hormones, Panic disorder, Parathyroid glands, Perceptual patterning, Personality disorders, Phobic disorder, Pitch, Polygenic traits, Positive reinforcer, Preoperational stage, Proactive interference, Psychiatrist, Psychodrama, Psychological motive, Psychophysics, Psychosurgery, Rapid eye movements, Reaction range, Recessive gene, Refractory phase, Reinforcement, Response, Retinal size, Scapegoat, Secondary sex characteristics, Selfperception, Semicircular canals, Septal area, Sex-role standards, Sibling, Smooth muscle, Sociology, Specific hunger, Stabilized retinal image, Stereoscopic vision, Stimulusresponse (S-R) psychology, T-maze, Temperament, Test profile, trait theory, Transsexual, Unconditioned response (UR), Variable, Visual area General terms. Additive mixture, Apparent motion, Attribution, Average, Concept, Control processes, Distance cues, Drive, Home sign, Left hemisphere, Modeling, Object constancy, Program, Right hemisphere, Simulation, Working through Other. Hue, Maze, Thinking Economics text. Ulmer, Melville J. Economics: Theory and Practice. 2nd ed. Boston: Houghton Mifflin, 1965. Neologisms. Engel's Law, Laissez faire, Lorenz curve, Oligopsony Technical terms. "Unfavorable" balance of t r a d e , Administrative bud g e t , Arbitration, Average c o s t , Balance of payments, Bank reserves, Boycott, Business cycles, Capital consumption, Capitalization, Common stocks, Compensatory fiscal policy, Constant costs, Countercyclical fiscal policy, Creditor nation, Crude death rate, Current assets, Debtor nation, Deflation, Deposit money, Depression, Diminishing Marginal Utility (law of), Disposable income, Dollar shortage, economic growth, Excess reserves, Factors of p roduction, Featherbedding, Fiscal policy, Fixed costs, Franchise, Full employment, Geometric progression, Gold standard, Gross investment, Holding company, Import quota, Innovation, Interlocking directorate, Least-cost combination, Liquidity preference, Marginal efficiency of capital, Marginal propensity to s a v e , Marginal revenue product, Market period, Mediation, Mixed enterprise capitalism, Monopoly, national debt, Natural resources, Net national debt,
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Normal profit, Operations research, Overhead costs, Patent, Personal tax, Primary deposits, Producers' good, Progressive t a x , Propensity to s a v e , Public utility, Pure monopoly, Real cost, Rediscount, Revenue, Saving function, Secondary boycott, Transfer payment, Turnover tax, Value added, Velocity of circulation General terms. Acceptance, Closed shop, Index number, Short run, Time preference Other. Assets, Consumption, Economics, Productivity, Supply, Technology Physics text. Sears, Francis W . , Mark W. Zemansky, and Hugh D. Young. University P h y s i c s . 6th ed. Reading, MA: Addison-Wesley, 1982. Neologisms. Adiabatic, Accelerometer, Ampre's law, Boltzmann constant, Boyle's law, Carnot c y cle, Corpuscles, Einstein's principle of relativity, Fermat's principle of least time, Fraunhofer diffraction, Isotopes, Lenz's law, Lloyd's mirror, Lorentz transformation equations, Ohmeter, Oscilloscope, Pitot tube, Wheatstone bridge Technical terms. Absorption spectrum, Acceleration of gravity, Atomic number, Ballistic pendulum, Blind spot, Capacitance reactance, Center of gravity, Central-field approximation, Circle of reference, Classical o p t i c s , Coefficient of linear expansion, Conic section, Conservative force, Constant acceleration, Current, Daughter nucleus, Dielctric strength, Diffraction, Diffuse reflection,
Drift velocity, electrical potential, Electromagnetic force, Empirical, Energy density, Equilibrium, Equivalent resistance, First law of thermodynamics, Fluid statics, Focal p o i n t , Force of static friction, Heat capacity, Heat engine, Horizontal range of projec t i l e , Hydrotherapy, Ideal f l u i d , Impedance of free s p a ce , Induced motional electromotive force, Limit of resolution, Magnetic m o m e n t , Magnetic susceptibility, Molar heat capacity, Optical resonance, Parallel-axis theorem, Polarizing a n g l e , Principle of conservation of c h a r g e , Principle of superposition, Quantum number, Radian, Rectangular component vectors, Rectilinear motion, Reflected pulse, Resolution of camera l e n s , Scalar qua n t i t y , Slope (of the chord p q ) , State coordinate, Surface integral, Time constant, Transformer, Universal conservation law, Vapor pressure, Vector General terms. Compliance, Dislocation, Disorder, Impulse, Overdamping, Virtual object, Wave packet Other. Pitch, Power, Radiation, Tension, Wave, Work Ecology text. Colinvaux, Paul. Introduction to Ecology. New York: John Wiley and Sons, 1973. Neologisms. Allopatry, Aufwuchs, Benthos, Commensalism, Cryptophyte, Ecotone, Eolian deposit, Forb, Infauna, Loess, Mycorrhizae, Neuston, Paleolimnology, Periphyton, Pheromone, Saprophage Technical terms. Productivity
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C Van Dyke

File: binary-jargon-the-metaphoric-language-of-computing.pdf
Title: Binary Jargon: the Metaphoric Language of Computing
Author: C Van Dyke
Author: Van Dyke, Carolynn
Published: Mon Jan 1 00:00:00 1601
Pages: 8
File size: 0.31 Mb

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