What question did this study set out to answer?

This research aims to explore how AI summarizers affect the reception of cultural texts.

June 18, 20269 citationsOpen Access

The Summarizer as Horizon of Reception: The AI Overview Capture Registry as a Founding Dataset for Machine-Mediated Reception Studies (EA-SEI-MMRS-DATASET-01 v1.2)

Key Points

This research aims to explore how AI summarizers affect the reception of cultural texts.
Introduces the AI Overview Capture Registry with 131 documented captures of AI-generated summaries.
Identifies six recurring patterns of summarization and their frequencies.
Analyzes the impact of AI summaries on cultural understanding without requiring the machine to comprehend the texts.
Morphological compression occurred in 29% of cases, altering the original cultural meanings.
Epistemic hedging was identified in 16% of summaries, showcasing uncertainty in information presentation.
Minority interpretations became mainstream, such as a notable reinterpretation of Sappho Fragment 31.

Abstract

The Summarizer as Horizon of Reception. This paper introduces machine-mediated reception studies: the study of how machine systems receive, transform, compress, attribute, misattribute, and redistribute cultural meaning. Its central claim is that the AI summarizer now functions as a horizon of reception (Jauss 1982): for many users, the machine-composed summary becomes the first effective encounter with a cultural object. As a founding dataset, the paper presents the AI Overview Capture Registry (EA-WG-CAPTURES-01 v7.2, DOI 10.5281/zenodo.20707509), 131 documented captures of Google AI Overview, AI Mode, and knowledge-panel responses. Six recurrent patterns are identified with frequencies and classical reception analogues: morphological compression (29%), disambiguation failure (15%), compositional bystanding (6%), canonical reinflation (12%), temporal drag (2%), and epistemic hedging (16%). In one case, a minority scholarly interpretation of Sappho Fragment 31 is now presented as the default understanding. Files: EA-SEI-MMRS-DATASET-01-v1.2.pdf (232 pages, 221 embedded screenshots), full markdown, standalone article (no appendix, for LLM review), coded-patterns.json (131 pattern-coded entries), MMRS-workplan.md (journal planning document). This paper uses "reception" in a functional rather than phenomenological sense: the machine does not need to "understand" Sappho in order to alter Sappho's reception. Publication venue: Machine-Mediated Reception Studies (Preprint). machinemediation.org (forthcoming).

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