• “It is interesting to note in this context that one of the earliest uses to which a commercial computer was put was as an engine of prediction: the UNIVersal Automatic Computer (UNIVAC), the first digital electronic computer to debut in the United States, successfully predicted the outcome of the 1952 Eisenhower­-Stevenson presidential race. The computer’s pedigree in symbolic logic, model­based reasoning, and data processing make it a predictive machine par excellence” (para. 2).
  • “Broadly conceived, then, this essay re­imagines the role of conjecture in textual scholarship at a time when computers are increasingly pressed into service as tools of reconstruction and forecasting. Examples of conjecture include the recovery of lost readings in classical texts and the computational modeling of the evolution of a literary work or the descent of a natural language. Conjectural criticism is thus concerned with issues of transmission, transformation, and prediction (as well as retrodiction)” (para. 4).
  • “Expressed in grammatical terms, conjecture operates in the subjunctive and conditional moods. Because its range of motion extends beyond the pale of the empirical, its vocabulary is replete with coulds, mights, mays, and ifs. Such a vocabulary reflects not caution—on the contrary, conjecture is a radical, audacious editorial style—but rather a refusal to settle for attested states of texts. In this it is opposed to the current wave of archival models of editing, which, in their rejection of speculative and inferential readings, opt for representation in the indicative mood” (para. 9).
  • “For the purposes of this essay, I’ll define it as follows: conjecture is allographic inference to past or future values of the sign. . . . By ‘allographic inference, then, I mean the considered manipulation or processing of digital signs with the goal of either recovering a prior configuration or predicting a future or potential one” (para. 16).
  • “Kac’s elaborate game of code­switching is enabled by what Matthew Kirschenbaum calls ‘formal materiality,’ the condition whereby a system is able to ‘propagate the illusion (or call it a working model) of immaterial behavior.’ Because this model is ultimately factitious, succeeding only to the extent that it disguises the radically different material substrates of the systems involved, it cannot be sustained indefinitely. But at some level this is only to state the obvious: abstractions leak, requiring ongoing regulation, maintenance, and modification to remain viable. This fundamental truth does not diminish the power or utility—what I would call the creative generativity—of models” (para. 21).
  • “Impressionistically, then, it can often feel as though the play’s textual ‘corruptions’—easily confused with its fictional vocal corruptions—were originating from within the story rather than from without; as if the text were inverting itself, such that the source of error were imaginary rather than real, with the fool becoming not only the object of a flawed transcription but also—impossibly—the agent of it” (para. 25).
  • ” Poets, like artists in general, often creatively stress­test the system or medium with which they work, probing its edges, overloading it, and pushing it beyond normal operational capacity. Discovering where language breaks down or deviates from regular use is the business of both poet and neuroscientist, providing a means of gaining insight into the mechanisms of language perception, processing, and production. But whereas the neuroscientist gathers data from aphasic patients in a clinical setting, the poet becomes, as it were, his own research subject, artificially manipulating the cognitive networks of meaning and sound that form his dataset” (para. 35).