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  1. Dec 2018
    1. Understanding how traditional textual devices function is especially important now when we are trying to imagine how to optimize our new digital tools. Manuscript and print technologies – graphical design in general – provide arresting models for information technology tools, especially in the context of traditional humanities research and education needs. To that end we may usefully begin by making an elementary distinction between the archiving and the simulating functions of textual (and, in general, semeiotic) systems. Like gene codes, traditional textualities possess the following as one of their essential characteristics: that as part of their simulation and generative processes, they make (of) themselves a record of those processes. Simulating and record keeping, which are co-dependent features of any autopoietic or semeiotic system, can be distinguished for various reasons and purposes. A library processes traditional texts by treating them strictly as records. It saves things and makes them accessible. A poem, by contrast, processes textual records as a field of dynamic simulations. The one is a machine of memory and information, the other a machine of creation and reflection. Each may be taken as an index of a polarity that characterizes all semeoitic or autopoietic systems. Most texts – for instance, this chapter you are reading now – are fields that draw upon the influence of both of those polarities. The power of traditional textualities lies exactly in their ability to integrate those different functions within the same set of coding elements and procedures. SGML and its derivatives are largely, if not strictly, coding systems for storing and accessing records. They possess as well certain analytic functions that are based in the premise that text is an "ordered hierarchy of context objects." This conception of textuality is plainly non-comprehensive. Indeed, its specialized understanding of "text" reflects the pragmatic goal of such a markup code: to store objects (in the case of TEI, textual objects) so that they can be quickly accessed and searched for their informational content – or more strictly, for certain parts of that informational content (the parts that fall into a hierarchical order modeled on a linguistic analysis of the structure of a book).

      voici où l'idée de Pierre Lévy et IEML pevent faire la différence. Ou pas?

  2. Sep 2018
    1. Lojban is probably the constructed language closest to IEML (it is univocalwith a non-ambiguous syntax), but it was designed to be spoken and it doesn’thave computable semantics
    2. The IEML Manifesto

      The IEML Manifesto; Information Economy Meta Language; Pierre Lévy; ©Pierre Lévy, License CC BY-NC-ND 4.0; Version 6 Août 2018

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    1. IEML Grammar

      Pierre Lévy 00-0-IEML_Grammar-en.pdf

    2. 3.2.2 The three classesAll natural languages contain differentclassesof units of meaning such as :nouns, verbs, adverbs, adjectives, prepositions, conjunctions, markers of gender,number, case, tense, mode, person, etc. The classes of verbal and nominal unitsare universal. Moreover it must be noted that there exist not only words butalso propositions that are verbal or nominal. In IEML, there exist only threeclasses of units in the paradigmatic and syntagmatic circuits : verbal, nominaland auxiliary units, but these classes can translate all those of natural languages.
    3. Consider for example the phrase “a student learns mathematics”*(y.a.-’ b.-j.-s.y.-’ E:E:T:.-’+y.a.-’ d.a.-s.a.-f.o.-’ E:E:S:.-’)**TermsE:E:S:.nominative (subject)E:E:T:.accusative (object complement)y.a.-learnb.-j.-s.y.-’mathematicsd.a.-s.a.-f.o.-’student1. The first clause (*y.a.-’ b.-j.-s.y.-’ E:E:T:.-’*) says that “mathematics” arepredicated on the verb “learn” on the mode of “the object”.2. The second clause (*y.a.-’ d.a.-s.a.-f.o.-’ E:E:S:.-’**) says that “the student”is predicated on the verb “learn” on the mode of the “subject”.The preceding example illustrates the three rolesin the syntagmatic circuits

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    1. I decidedthat verbals would be represented by vowels, and nominals would be represented byconsonants. Since my combinatory ended up with 10 vowels and the Romanalphabet only has six, I adopted long vowels (wo, wa, wu, we) to avoid causingproblems for users whose keyboards had no accents.
    2. The Semantic Sphere 1; Computation, Cognition and Information Economy; Pierre Lévy

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  3. Oct 2017