Delayed closure is developed as a practice of keeping recognized interpretations revisable and five public requirements concerning materials and versions, evidential roles, failure, contract revision, and responsibility are proposed.
Abstract
Generative AI research has increasingly evaluated factuality, citation, coverage, and report structure. Yet passing such local checks does not by itself show that a humanistic interpretation has been established. This paper asks how an interpretation comes to be recognized within sociotechnical processes. It introduces three connected concepts. Interpretive appearance names the gap between the finished form of an output and the publicly traceable process through which materials, counterevidence, and revisions constrained the judgment. The evaluation contract names the bounded materials, tasks, criteria, permitted inferences, and failure conditions within which a local judgment is valid. Standing substitution names the unwarranted conversion of a genuine local pass into a stronger claim that an interpretation, result, or research capability has been established, without commensurate new evidence or bridging arguments. The paper then examines responsibility for judgment: a text may acquire recognition while no public structure remains for stating reasons, answering objections, revising, downgrading, or withdrawing the conclusion. Humanistic scholarship provides a revealing test because new materials and conceptual distinctions can alter both the question and the criteria of evaluation. The paper therefore develops delayed closure as a practice of keeping recognized interpretations revisable and proposes five public requirements concerning materials and versions, evidential roles, failure, contract revision, and responsibility. The argument is conceptual and normative: it does not claim to offer a benchmark or to determine whether models possess understanding. It instead explains why local evaluation, finished textual form, and public recognition must not be treated as sufficient evidence that an interpretation has been formed.
This article argues that “AI use” is too blunt a category for judging what happens when a student writes a humanities paper with a large language model. Taking the most intensive case, a paper built with AI from research to final review, it follows the changing relation between student, model, sources, and draft. In re...
Joshua Leonardo Lupowitz· Tiempo devorado· 0 citations
An argument can be surprising coming from a particular speaker without being a bad argument. Do AI evaluators keep these judgments apart? Two preregistered descriptive studies and a later Jev supplement collected 2,976 usable evaluations of six fixed texts about US AI policy, Germany's debt brake and Swiss nuclear ener...
A research team can hand its budget to partners in the Global South, list those partners first on the paper, and let them set the research questions. The concepts, methods and terms that decide what counts as a finding stay in place. More manuscripts now claim to be decolonial, and editors and reviewers judge those c...
M. Patka, Masi Noor· Journal of Community & A...· 0 citations
This article explains how material practices and naming shape collective agency in organizations that depend on artificial intelligence. It addresses an apparent tension. An AI system can be a constitutive condition of what an organization can do at a given time. The same system can remain replaceable across the organi...
The result concerns the scope of cognitive attribution rather than the constitutive conditions of concept possession: cognitive achievements deserve credit for the capacities they establish, without silently importing a broader repertoire through the labels attached to them.
The contribution is not a more accurate judge, but a label-free way to tell when a judge has no basis for its answer, which is a label-free way to tell when a judge has no basis for its answer.
S. Aly, Hussein Assaf, Ziad Kobti· 0 citations
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