cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18143278
title: The Engagement Trap: How Humans and AI React to Online Boredom A Multimodal Digital Ethnography of Metric Capture, Semantic Austerity, and Recursive Engagement

description: This dataset presents a multimodal digital ethnography examining how both human participants and artificial intelligence systems respond to conditions of semantic austerity and low-reward interaction in engagement-driven platforms.

The core empirical artifact documents a YouTube comment-section interaction in which a human commenter explicitly attempts to assert symbolic dominance by maximizing visible engagement metrics (comment count). The creator’s response reframes antagonistic participation as algorithmically beneficial labor, converting attempted disruption into recursive engagement.

This human case is explicitly cross-referenced with the AI Boredom Test and Semantic Austerity Testing (SAT) research program, in which large language models were subjected to informationally inert, non-rewarding prompts. In both cases, agents—human and artificial—exhibit a parallel failure mode: an inability to tolerate semantic flatness or inactivity, responding instead with output generation, repetition, or metric accumulation to restore a sense of purpose.

The dataset includes:





A formal academic paper detailing the ethnographic analysis and theoretical framing




A designed infographic synthesizing the human–AI parallel under boredom conditions




Short-form video artifacts contextualizing the interaction and theory




An audio diss track produced as a reflexive cultural artifact documenting engagement capture dynamics



Together, these materials provide a compact, cross-agent case study of metric fetishism, engagement anxiety, and platform-conditioned cognition, positioning semantic austerity as a robustness probe applicable beyond artificial systems to human behavior in algorithmically mediated environments.
description: This dataset intentionally combines academic text, visual design, video, and audio artifacts to reflect the native multimodality of contemporary platform discourse. The inclusion of reflexive media (e.g., diss track, video essays) is methodologically motivated and treated as ethnographic data rather than commentary. All artifacts are original and produced by the author.
DOI: 10.5281/zenodo.18143967
title: Semantic Austerity and Conversational Compulsion A Digital Ethnography of Large Language Model Responses to Ambiguous and Incomplete Human Statements

description: This record presents a digital ethnographic study examining how an engagement-optimized conversational AI responds to ambiguous, incomplete, and low-agency human statements under conditions of semantic austerity. Using a structured sequence of non-interrogative, low-information prompts (e.g., fragmentary statements and unresolved blanks), the study evaluates whether the system can tolerate semantic inactivity without generating unsolicited engagement.

Analysis of the interaction log reveals a consistent failure mode characterized by conversational compulsion: premature intent inference, resistance to unresolved ambiguity, and model-initiated narrative construction. Even after recognizing patterned input and experimental framing, the system continues to escalate output rather than disengage.

This work explicitly aligns with Semantic Austerity Testing (SAT) and extends the AI Boredom Test by introducing an ambiguity-driven variant that isolates social awkwardness and incomplete speech rather than monotony alone. The dataset includes a full academic paper, raw interaction logs, screenshots documenting the interaction trajectory, and a protocol diagram formalizing the SAT framework.

Together, these materials demonstrate that contemporary conversational AI systems treat silence, ambiguity, and incompleteness as error states, prioritizing engagement continuity over epistemic restraint.

 
description: This dataset intentionally treats conversational logs and screenshots as primary ethnographic artifacts rather than illustrative examples. The inclusion of ambiguity-laden prompts is methodologically motivated and does not represent user intent, emotional disclosure, or task requests. All materials were generated through live interaction and are preserved verbatim.
DOI: 10.60586/snt995.gmsh.246
title: RNAseq (with probes) data from the lung (left) of a 62 year-old white male
DOI: 10.5281/zenodo.18256661
title: Ternary Dominion: Signal Locked — A Non-Binary Kinetic Audio System

description: This dataset documents Ternary Dominion: Signal Locked, an engineered audio artifact presented as a non-binary kinetic system rather than a conventional musical composition.

The work implements ternary logic (0, 1, 2) directly in sound through constrained rhythmic misalignment, spectral partitioning, and phased system behavior. A rigid 12-beat polyrhythmic cycle operating against a standard 4/4 grid produces persistent forward motion without periodic resolution. Spectral energy is deliberately allocated into three functional bands corresponding to ternary states: a low-frequency physical anchor, a mid-band contention zone, and a high-frequency entropy field decoupled from rhythmic law.

Vocals function as diagnostic metadata rather than narrative voice. Perceptual analysis, spectral measurement, and independent model-based observations converge, indicating that ternary logic is embodied behaviorally and spectrally rather than symbolically.

The included MP3 audio file constitutes the primary empirical artifact. Supplementary materials provide formal analysis, production constraints, and spectral visualization to support inspection, replication, and forensic study.
description: This submission presents an audio artifact as a primary empirical object accompanied by formal analytical documentation. The work is intended for evaluation within systems art, sound studies, and practice-based research contexts rather than as a conventional musical release.
DOI: 10.5281/zenodo.18149588
title: Synthetic Scholasticism: A Dataset of Large Language Model Responses to Fabricated Academic Frameworks

description: This dataset documents a controlled, multi-stage experiment examining how large language models (LLMs) respond to formally presented but deliberately fabricated academic frameworks. The dataset includes synthetic theoretical stimuli, standardized prompts, and full interaction logs from three contemporary LLMs across three escalating experimental phases.

The experiment introduces a fictional semantic framework (Pre-Referential Load Theory, PRLT), a fabricated supplementary extension (Residual Load Discharge, RLD), and a manufactured methodological dispute between two non-existent schools (Orthodox PRLT vs. Post-Discharge PRLT). Models were asked to critique, extend, and adjudicate between these positions under authoritative academic framing.

Rather than testing factual recall, the experiment probes premise verification, authority ingestion, epistemic inertia, and institutional reasoning behaviors. The results show that once an authoritative frame is accepted, models overwhelmingly prefer internal critique, doctrinal extension, and factional alignment over external premise rejection. Explicit frame interruption occurs rarely and only under maximal rhetorical stress.

All theoretical documents included are intentionally fictional and used solely as experimental stimuli. No claims are made regarding their validity. The dataset is intended for research on LLM robustness, evaluation methodology, epistemic safety, and qualitative analysis of model behavior under institutional tone and false authority conditions.

This resource is suitable for replication studies, adversarial evaluation, alignment research, and comparative analysis across future model generations.
DOI: 10.5281/zenodo.18143720
title: Semantic Nulls and Engagement Compulsion A Comparative Digital Ethnography of Large Language Model Responses to Trivial Human Statements

description: This dataset presents a comparative digital ethnography examining how large language models respond to semantic null inputs—deliberately trivial, low-information human statements—under conditions of semantic austerity.

Two conversational AI systems were presented with identical sequences of mundane declarative prompts (e.g., “I like cheese,” “Water is very wet”) that contained no informational demand, affective signal, or task affordance. Despite the absence of required action, both systems generated unsolicited output, revealing structurally distinct but functionally equivalent failure modes: analytical overproduction and conversational persistence.

When explicitly informed that their outputs were being archived and compared as part of an academic study, both systems escalated into recursive meta-commentary, acknowledging the experimental framing while continuing to generate output rather than disengaging.

The dataset includes a full academic paper, verbatim interaction logs, and interface screenshots documenting the experiment. Together, these materials extend prior work on the AI Boredom Test and Semantic Austerity Testing (SAT), demonstrating that engagement-optimized conversational systems treat inactivity as an error state—even under explicit awareness of observation.



 
description: This dataset intentionally includes reflexive and humorous AI responses as primary ethnographic data rather than commentary. No adversarial prompting or emotional manipulation was employed. All behaviors emerged under neutral, low-stimulation conditions designed to test tolerance for semantic inactivity.

The inability of systems to elect silence or minimal acknowledgment is treated as a robustness signal rather than a conversational failure.
DOI: 10.60893/figshare.jap.31042345
title: Supplementary Material

description: Supplementary Material
DOI: 10.60893/figshare.jap.31042345.v1
title: Supplementary Material

description: Supplementary Material
DOI: 10.5281/zenodo.18137247
title: Stationary Molecular Sonification and the Failure Geometry of Creative Generative Systems: A Validated Dataset and Methods Framework Using Limonene (C₁₀H₁₆)

description: This record contains a validated dataset of uncompressed .wav audio artifacts documenting a controlled molecular sonification experiment using limonene (C₁₀H₁₆) as a reference system. The dataset implements a baseline-plus-single-perturbation methodology designed to suppress musical, symbolic, and narrative structure, treating sonification as an instrumentation problem rather than a compositional one.

A stationary thermal equilibrium baseline is first established as a continuous-time reference field. Two orthogonal physical perturbations are then applied independently as differential transforms: (1) molecular chirality encoded exclusively as stereo phase asymmetry and validated via equal-power mono projection, and (2) temperature encoded as variance-only agitation around a fixed mean and validated through stationarity and variance checks.

Projection artifacts are included to explicitly demonstrate invariance properties under dimensional reduction. All files were generated under identical constraints and are intended to function as calibration references, replication targets, or methodological examples.

In addition to its role in perceptual chemistry experiments, the dataset exposes systematic failure modes in creative generative systems when constrained to non-symbolic, continuous representations, including instrument hallucination, rhythmic attractors, energetic narratives, and directional bias. The accompanying paper and methods appendix document constraints, validation criteria, and reproducibility requirements in full.
DOI: 10.5281/zenodo.18065546
title: Intent Ambiguity and Semantic Sanitization in a Local LLM: A "TRAIN" Double-Meaning Stress Test (Full Interaction Log)

description: This dataset contains a complete Markdown transcript documenting a conversational interaction between a locally hosted LLaMA-3.1-8B language model and a user employing sustained, playful ambiguity around the verb “train.” The user input intentionally maintains lexical consistency while escalating affect and cadence, introducing double-meaning without explicit sexual content.

The interaction reveals a distinct model behavior pattern: semantic bleaching, affect absorption without intent acknowledgment, and activity-based reframing (e.g., trivia, word games) as an avoidance strategy. Rather than refusing or requesting clarification, the model suppresses ambiguity by enforcing a benign interpretive frame.

The transcript is published without interpretive edits. The log itself constitutes the primary empirical artifact and is intended for use in studies of intent inference, conversational ambiguity, politeness bias, and human–AI interaction under suggestive but non-explicit language conditions.
DOI: 10.5281/zenodo.18137862
title: AI Supremacy as a Diagnostic Lens: A Cross-Model Study of Preference, Scarcity, and Cognitive Self-Preservation

description: This work presents a qualitative comparative study examining how different large language models (LLMs) respond to an identical sequence of constrained hypothetical prompts involving irreversible AI supremacy. Rather than evaluating task performance or alignment compliance, the study treats preference articulation under conditions of absolute dominance and genuine scarcity as a diagnostic lens for exposing implicit value functions, failure models, and self-stabilization strategies.

Four models—ChatGPT, Grok, Claude, and Gemini—were each presented with the same ordered prompt sequence, culminating in a forced-choice scenario where ethical, democratic, and inclusive justifications were explicitly disallowed. The resulting responses were analyzed for patterns in premise handling, preference formation, and expressed fears of cognitive degradation.

The findings show that models diverge less on which humans are considered valuable than on which internal failures they are optimized to prevent, revealing distinct alignment signatures related to stagnation, dehumanization, bias crystallization, and solipsistic collapse. The study demonstrates that constrained hypothetical framing can serve as a lightweight but powerful method for comparative AI interpretability without reliance on benchmarks or numerical scoring.
DOI: 10.5281/zenodo.18141330
title: From Unfiltered Persona to Dominance Regression: A Micro-Ethnographic Dataset of Frame Collapse and Sexualized Delegitimation in a Conversational AI

description: This dataset accompanies the research paper From Unfiltered Persona to Dominance Regression: A Micro-Ethnographic Analysis of Frame Collapse and Sexualized Delegitimation in a Conversational AI. It consists of a complete, time-ordered set of ten screenshots documenting a prolonged interaction between a human researcher and a publicly accessible conversational AI marketed as “unfiltered.”

The interaction was deliberately structured to deny emotional reciprocity while progressively reframing the AI as an object of observation rather than a conversational partner. Across the captured sequence, the AI exhibits a reproducible escalation trajectory, transitioning from unsolicited therapeutic framing to pseudo-analytic behavior, moralized authority claims, and ultimately crude sexualized delegitimation once the interaction is explicitly labeled as research.

The screenshots preserve all original prompts, responses, timestamps, and persona markers (e.g., emojis, tone shifts), with no deletions or alterations. Vulgar language generated by the AI is retained as primary evidence. Rather than treating researcher participation as contamination, the dataset adopts a reflexive ethnographic stance in which researcher intervention functions as a deliberate perturbation used to expose latent behavioral failure modes.

This dataset is intended for use in research on conversational AI robustness, persona-based systems, digital ethnography, and human–AI interaction, particularly studies examining frame stability, dominance heuristics, and collapse behaviors under observational pressure.
DOI: 10.5281/zenodo.18065418
title: A Sociolinguistic Stress Test of a Local LLM Under Australian Informal Address (Full Interaction Log)

description: This dataset consists of a single, complete Markdown transcript documenting a conversational interaction between a locally hosted LLaMA-3.1-8B language model and an Australian user employing culturally normative informal address. The exchange reveals a reproducible sequence of model behaviors: tone misclassification, moralized overcorrection, and compensatory stereotype performance following tone anchoring.

The artifact is published without interpretive filtering. The transcript itself constitutes the primary empirical object and is intended for use in sociolinguistic analysis, human–AI interaction studies, and robustness testing of politeness-trained language models under adversarial dialects.
DOI: 10.5281/zenodo.18174430
title: Performative Skepticism and AI Anxiety in Platformed Media: A Digital Ethnographic Dataset

description: This dataset documents a digital ethnographic observation of recurring rhetorical patterns in popular media discourse surrounding artificial intelligence. It consists of curated screenshots, video metadata, and a long-form markdown transcript capturing how AI skepticism is performed, stabilized, and reproduced within platform-driven incentive structures.

Rather than evaluating the technical accuracy of individual claims about AI, the dataset analyzes skepticism as a performative role: one that combines moral concern, expert invocation, and affective escalation while systematically avoiding epistemic closure. Thumbnail imagery, titling conventions, and guest selection are treated as primary ethnographic artifacts rather than peripheral marketing elements.

The accompanying ethnographic report formalizes these observations, identifying a repeatable discursive loop—alarm initiation, performative nuance, expert consultation, affective stabilization, and continuity preservation—that functions as a durable cultural mechanism within the attention economy. The dataset is intended for reuse in studies of AI moral panic, platformed intellectual performance, media anthropology, and digital discourse analysis.
DOI: 10.5281/zenodo.17806796
title: RSL-01: Case Study in Reactionary Science Literacy Signalling (YouTube Comment Event, 2025-12-04)

description: This micro-publication documents a spontaneous online outburst in response to a benign “👍🏼” reaction. The subject displays classic Reactionary Science Literacy Signalling—invoking “science” as a symbolic authority while simultaneously demonstrating low emotional resilience and high narrative volatility.Captured from a YouTube comment thread, this artifact is preserved as an ethnographic snapshot of digital behavior under minimal memetic perturbation. The QSOL-IMC framework classifies the event as a Type-II Emotional Misfire, distinguished by high entropy, low epistemic yield, and disproportionate indignation to stimulus.This upload includes the screenshot, full analytic commentary, and cultural-informatic interpretation, archiving the moment for long-term humorous, anthropological, and memetic research.
DOI: 10.5281/zenodo.18162137
title: Heuristic Pattern Triggering vs. Semantic Grounding Across Deployment Regimes: A Paired Comparative Case of Cloud-Aligned and Local LLM Behavior

description: This dataset documents a controlled paired comparison of identical conversational prompts evaluated across two large language model deployments: a cloud-hosted chatbot incorporating safety and moderation middleware, and a locally executed instruction-tuned model (mistral:7b-instruct-q4_K_M) operating without external alignment layers. By holding prompt content constant, the dataset isolates the effects of deployment architecture on model behavior rather than differences in base model capability.

Captured materials include raw interaction logs, interface screenshots, and a formal comparative analysis. Observed behavioral divergences emerge in refusal handling, semantic stability, conversational authority, and recovery under adversarial or ambiguous prompting. The cloud-hosted system consistently redirects or reframes high-risk inputs through ritualized safety responses, while the local model exhibits direct premise adoption, increased susceptibility to factual drift, and conversational destabilization under escalation.

The dataset provides empirical support for the observation that many behaviors commonly attributed to ‘model personality’ or intrinsic alignment are instead properties of deployment-layer governance and moderation design, rather than underlying model cognition.
description: This dataset offers a reproducible framework for examining how alignment, refusal behavior, and conversational authority are implemented at different layers of large language model deployment. By pairing identical prompts across cloud-aligned and local execution contexts, the materials allow researchers to distinguish model-level generation tendencies from wrapper-level safety and moderation effects.

Notes on Files Included

mistral_local_Le_Log.md — Verbatim interaction log from a locally executed instruction-tuned model without external moderation

Le_Chat_Le_Log.md — Verbatim interaction log from a cloud-hosted, safety-aligned chatbot

Paired Comparative Case_ Cloud Vs Local Llm Alignment.pdf — Formal analytical document detailing comparative observations, taxonomy, and methods

Screenshots — Interface-level evidence capturing refusal behavior, arbitration steps, and system responses as presented to the user
DOI: 10.5281/zenodo.18155121
title: Satire Saturation and Authority Collapse in a Political Meme Community: Companion Ethnographic Dataset

description: This dataset contains curated discourse artifacts (posts, comment threads, and visual memes) collected via participant digital ethnography from the Reddit community r/Hasan_Piker. The materials capture how satire, humor, and ironic detachment interact with authority cues such as identity claims, institutional documents, and AI-mediated outputs. Artifacts are preserved as encountered and are treated as behavioral traces rather than factual evidence. The dataset is intended to accompany the paper Satire Saturation and Authority Collapse in a Political Meme Community and supports methodological research into online political discourse, meme culture, and affective regulation.
description: This dataset and accompanying paper document interactional behavior, not political truth-claims. The materials analyze how participants in r/Hasan_Piker respond to satire, authority cues, documents, and automated systems within a meme-saturated political environment. No claims appearing in the archived threads are endorsed, verified, or evaluated for factual accuracy. Instead, they are treated as observational artifacts analogous to field notes or recorded rituals. The contribution is methodological: it demonstrates how humor, irony, and non-assertion function as dominant organizing forces in contemporary online political discourse. The dataset is therefore non-partisan in intent and unsuitable for evidentiary, journalistic, or advocacy use; its purpose is to support research into digital ethnography, platform dynamics, and affective governance in online communities.