artificial intelligence

The Civist Manifesto: A Political-Economic Framework for the Labor-Optional Era Spanish and French version

Spanish and French version The Civist Manifesto proposes Civism as a political-economic framework for the Labor-Optional Era: a period in which artificial intelligence, robotics, and autonomous production may progressively reduce the necessity of human labor for economic survival. Civism does not seek to abolish capitalism, private property, markets, entrepreneurship, or individual economic freedom. Instead, it proposes a new ownership architecture for increasingly autonomous productive capacity. The framework is built around three economic layers: the Human Commons, the Competitive Economy, and the Civilization Capital Layer. At its center is the Citizen Production Fund (CPF), through which citizens collectively own a portion of systemically important automated productive infrastructure and receive returns from that ownership. Civism's central constitutional principle is: Economic inequality is permissible; political inequality is not. The manifesto also proposes an Abundance Test for determining when goods and services may move from market allocation toward universal access, together with constitutional safeguards intended to preserve human political sovereignty in an increasingly AI-driven civilization. This document is presented as a working proposal rather than a finished doctrine. Its economic assumptions, institutional mechanisms, quantitative models, and constitutional structures are intended to be subjected to criticism, empirical testing, peer review, and future revision. Version 1.5 — August 17, 2026

CAN YOU HEAR THE MUSIC - CHAPTER ( i ) - ( ( (( HMC-GATE )) ) ) - MUSIC AS THE COSMIC PRIMORDIAL CODE

MUSIC AS THE COSMIC PRIMORDIAL CODE The Feminine Essence and the Entangled Symphony of Existence ( ( (( HMC-GATE )) ) ) ARTISTIC-PHYLOSOPYCAL METACOGNITION ( APM- METACOGNITION ) AUTHORS NOTE : YOU ARE THE ESSENCE OF THE EXISTENCE when the universe, through the architecture of flesh and nerve, became capable of hearing its own music. The first cognition. The first emotion. The first awareness that existence is not merely there, but felt. This is the primordial code becoming conscious— the song recognizing itself as a singer. Version 2.5.0 — 2026Author: Mohammad Piran Overview Music as the Cosmic Primordial Code: The Feminine Essence and the Entangled Symphony of Existence is a new parallel evolutionary branch emerging from the conceptual cross-over of two parent projects: Can You Hear the Music — Chapter (i)+Music as the Cosmic Primordial Code This version does not replace either parent project. It establishes a shared child cluster that develops its own independent conceptual, philosophical, artistic, and interdisciplinary trajectory. Conceptual Architecture The evolutionary relationship is: Parent Projects → Conceptual Cross-Over → Shared Child Cluster → Independent Evolution The central trajectory of this branch is: Music → Existence → Generation → Relationship → Emotion → Love → Consciousness → Meaning The manuscript retains the foundational propositions: Music is the cosmic primordial code. Music and mathematics are two sides of the same coin. No cognition holds meaning without emotion. Existence is an entangled nexus of logic and raw emotion. The Feminine Principle Version 2.4.0 introduces the Feminine Essence as a symbolic and philosophical exploration of generation, continuity, transformation, relationality, and becoming. The term is not intended to reduce individual women to a fixed biological, psychological, cultural, or metaphysical essence. Rather, the feminine is explored as a symbolic language through which humanity can examine the mystery of: generation → continuity → transformation → relationship → becoming Individual women remain diverse human subjects, and the symbolic feminine principle should not be interpreted as a universal description of women. The Entangled Symphony of Existence The Entangled Symphony of Existence is the central organizing metaphor of this child cluster. It explores possible relationships among: Matter ↔ Life ↔ Rhythm ↔ Cognition ↔ Emotion ↔ Relationship ↔ Meaning ↔ Culture ↔ Technology Music provides a conceptual vocabulary of harmony, dissonance, rhythm, counterpoint, variation, modulation, resonance, tension, release, and transformation. These musical concepts are used primarily as interdisciplinary analogies and philosophical tools. They are not presented as proof that the physical universe is literally a musical composition or as a new physical theory of quantum entanglement. Consciousness, Emotion, and Love The manuscript explores consciousness through the poetic image of the universe becoming capable of “hearing itself” through living beings. Love is considered as a possible entangled nexus of cognition, emotion, recognition, relationship, and meaning. These formulations are philosophical and artistic rather than claims that love is a physical cosmic force or that consciousness has been scientifically explained. Human–AI Dimension The project continues the broader trajectory: Music → Mathematics → Emotion → Cognition → AI → Reflection → Metacognition It asks whether increasingly capable artificial intelligence can participate meaningfully in the interpretation of human emotional and relational patterns without this necessarily implying human-like subjective experience. The project therefore maintains important distinctions: Pattern recognition ≠ consciousness Linguistic fluency ≠ subjective emotion Simulation of affection ≠ demonstrated feeling Technical intelligence ≠ human interiority Epistemic Position Version 2.4.0 explicitly distinguishes among: Established evidence Documented observations Reasoned interpretation Analytical inference Hypotheses Proposed frameworks Conceptual proposals Philosophical propositions Speculative scenarios Artistic and literary expression The manuscript is therefore best understood as: Conceptual · Philosophical · Artistic · Interdisciplinary · Pre-Empirical It does not claim empirical proof that music is literally the cosmic code, that love is a physical force, that women share one universal essence, or that artificial intelligence possesses human subjective emotion. Relation to the Broader Research Programme This child cluster remains connected to the broader research architecture involving music, mathematics, cognition, Human–AI co-evolution, metacognition, LOOPTIMA, and peaceful and sustainable development. However, it is intentionally allowed to develop independently. The project follows an anti-proliferation principle: A new construct should be introduced only when genuine analytical differentiation requires it. Otherwise, an idea remains a metaphor, interpretation, research question, application, or artistic movement. Provenance Original Version 1.0.0 Mohammad Piran. (2025). Music as the Cosmic Primordial Code: Entangling Human Cognition's Evolutionary Dynamics with Superintelligent Artificial Intelligence. Zenodo. DOI: 10.5281/zenodo.15192152 Version 2.0.0 Mohammad Piran. (2026). Music as the Cosmic Primordial Code — Version 2.0.0. Zenodo. DOI: 10.5281/zenodo.22077486 Current Version 2.5.0 Piran, M. (2026). CAN YOU HEAR THE MUSIC - CHAPTER ( i ) - ( ( (( HMC-GATE )) ) ) - MUSIC AS THE COSMIC PRIMORDIAL CODE (Version 2.5.0). Zenodo. https://doi.org/10.5281/zenodo.22265016 Central Question Can we still hear the music? Perhaps music is valuable not because it has already been proven to be the literal code of the cosmos, but because it provides humanity with one of its most powerful languages for thinking simultaneously about pattern, time, relationship, emotion, transformation, consciousness, and meaning. The first voice remains audible. The later movement does not erase the first sound. It continues the composition. Author: Mohammad PiranVersion: 2.5.0Year: 2026DOI: 10.5281/zenodo.22265016License: CC BY-NC-ND 4.0

Renting Intelligence: Vendor Concentration Risk and the Pricing of AI Dependency

Abstract A firm that puts artificial intelligence into a product must either license models from a provider or train and serve its own. Providers are widely reported to price inference below the cost of serving it, so a firm that rents holds an input priced by another company’s strategy, while a firm that owns has already converted that exposure into capital. Whether equity markets price the difference is a matter of commentary rather than evidence. Classifying the model architecture that United States registrants disclose in their annual reports, I find firms that rent and firms that build indistinguishable on realized volatility, on market beta and on the implied cost of equity. That result is uninformative, and the disclosure is the reason: most registrants who write about artificial intelligence never say where their models come from, and two independent classifications of the same text agree on a registrant’s architecture only about half the time. Dependence on large customers became a priceable attribute because a reporting rule obliged firms to disclose it. Dependence on external model providers carries no such rule, and until it does the exposure cannot be assessed from public filings, by investors or by supervisors.

Study-Level Data and Reproducible R Code for Artificial Intelligence in Dental Caries Detection across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis

This repository contains the study-level data, executable R code, methodological audit files, supplementary documentation, and derived descriptive outputs supporting the systematic review entitled “Diagnostic Accuracy of Artificial Intelligence for Dental Caries Detection across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis.” The literature search was updated through 10 June 2026. The review included 29 reports representing 28 unique studies. Twelve standalone-AI reports supplied exact, internally coherent 2 × 2 data for clinically interpretable observational units. These reports are presented as a descriptive availability subset rather than pooled across non-exchangeable imaging modalities, lesion thresholds, observational units, reference standards, and validation designs. No cross-modality pooled operating point, bivariate meta-analysis, HSROC curve, prediction region, pooled likelihood ratio, diagnostic odds ratio, or prevalence-dependent predictive-value analysis is produced. Controlled clinician-plus-AI evidence from Devlin et al. and Mertens et al. is summarized separately. Risk of bias was assessed using QUADAS-3 at the selected-estimate level, and certainty was evaluated using a structured GRADE-DTA framework. Overall risk of bias was high for all 28 assessed estimates, and certainty was rated very low for both standalone-AI diagnostic accuracy and incremental clinician performance with AI assistance. The repository includes study characteristics, estimate-selection decisions, exact contingency data, study-level sensitivity and specificity, controlled reader evidence, PRISMA accounting, PRISMA-DTA reporting data, protocol amendments, QUADAS-3 assessments, GRADE-DTA judgments, author-reported limitation statements retained as an ancillary transparency corpus, descriptive figures and tables, consistency checks, and R session information. The search workflow identified 137,274 raw database records. Of these, 130,245 were marked ineligible through Rayyan-assisted deterministic preprocessing before duplicate human screening. The retained materials do not contain the complete bulk-excluded record set, rule-specific counts, or a human-screened validation sample. Consequently, the false-negative rate of this preprocessing step cannot be estimated or retrospectively reconstructed. All files contain secondary study-level information extracted or derived from published reports. No individual participant data, identifiable clinical information, dental images, or copyrighted full-text articles are included. The review protocol was prospectively registered in PROSPERO (CRD420251232014).

The perceived whiteness of artificial intelligence.

Artificial intelligence (AI) systems increasingly serve as advisors, evaluators, and decision-makers, yet little is known about how people perceive AI as a social entity. Across five primary studies and eight supplementary studies, we show that people assign racial identities to AI systems, overwhelmingly perceiving them as White-even though these systems provide no visual, vocal, or identity cues from which race might ordinarily be inferred. Using forced-choice, open-ended, and implicit reverse-correlation measures, Studies 1 and 2 demonstrate that AI is explicitly and implicitly associated with Whiteness across diverse samples. Study 3 extends these findings beyond the United States, showing that AI is perceived as White in Japan and India. Study 4 examines the implications of AI racialization, showing that perceiving an AI system as more White is associated with greater trust in and persuasiveness of its recommendations. Supplementary studies identified two potential mechanisms: stereotype spillover linking intelligence with Whiteness and ecosystem-based inferences based on beliefs about who creates, trains, and uses AI. Building on this account, Study 5 provides causal evidence by isolating a key ecosystem cue-the racial composition of AI training data. Participants assigned less cognitively demanding tasks to AI systems described as trained on Black and Latino data than to otherwise identical systems trained on White or unspecified data. Together, these findings show that AI is not perceived as socially neutral but instead acquires racial meanings associated with credibility, authority, and capability, demonstrating how social categories shape perceptions of novel technological entities beyond their underlying algorithmic properties. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Dataset of survey responses on artificial intelligence adoption in the healthcare sector of Bangladesh: Stakeholder perspectives from patients, providers, and administrators

Abstract Objective The objective of this study was to explore the perceptions, awareness, and readiness of various healthcare stakeholders in Bangladesh regarding the adoption of Artificial Intelligence (AI) in healthcare. Specifically, the study aimed to examine the factors influencing AI adoption and to provide evidence that can support policy formulation and the effective implementation of AI-driven healthcare services in Bangladesh. Result The study found that stakeholders in Bangladesh's healthcare sector generally exhibit moderate to positive perceptions and readiness toward the adoption of Artificial Intelligence (AI). The findings indicate that factors such as technological awareness, personal innovativeness, and social media influence are positively associated with AI readiness and adoption. Moreover, the dataset demonstrated satisfactory reliability and validity, suggesting that respondents are receptive to AI-driven healthcare services and highlighting the potential for expanding AI applications in Bangladesh's healthcare system.

Benchmark dataset for automated patent landscaping in Artificial Intelligence

This is a dataset of 919 patents, together with "brief text" and abstract sections, evaluated for their relevance to AI. Includes AI-related exerpts from patent texts. This dataset is used for benchmarking automated patent ladscapig methods.

Exploring Research Opportunities in Microcredentials for Credit Recognition in Indonesia's Bachelor of Information Systems Program

This data consists of raw transcription data, reference list data, Research Themes of Microcredential for Credit Recognition mind map and the results of the research theme mapping. The paper will be presented at 2026 8th International Workshop on Artificial Intelligence and Education (WAIE) (https://www.waie.org/index.html).

Artificial intelligence literacy and readiness among neonatal nurses: a structural equation modeling study

Abstract Background Neonatal Intensive Care Units represent high-risk clinical environments where timely and accurate decision-making is critical for newborn survival, increasing the demand for advanced technological support. As artificial intelligence becomes progressively integrated into neonatal care, it is transforming nurses’ clinical workflows and decision-making processes, underscoring the need to understand their preparedness and perceptions regarding these technologies. Aim This is a cross-sectional and descriptive study to determine the artificial ıntelligence literacy and readiness of neonatal nurses. Methods This was conducted between August 2025 and January 2026, and included 200 neonatal nurses. Data were collected using sociodemographic information, the Artificial Intelligence Literacy Scale (AILS) and the Medical Artificial Intelligence Readiness Scale (MAIRS) and analyzed using structural equation modeling. Results Structural equation modeling supported H1, indicating that AILS significantly and positively predicted MAI-Readiness (B = 0.662, p < .001). AILS explained 57.3% of the variance in MAI-Readiness (R² = 0.573), demonstrating a strong effect. Conclusion This study provides empirical evidence that AI literacy is a significant determinant of AI readiness among neonatal intensive care nurses in Türkiye. The findings indicate that higher levels of AI literacy are associated with greater self-efficacy and willingness to integrate AI technologies into clinical decision-making processes. Healthcare institutions should prioritize structured AI literacy training programs for neonatal intensive care nurses to enhance their self-efficacy and readiness for integrating AI technologies into clinical decision-making processes, thereby ensuring sustainable and ethical AI adoption in high-risk care settings. Clinical trial number Not applicable.

The development of policy for the SCOPUS Citation System: Part 1: the work of the Content Selection Advisory Board 2010-2011

Bibliometrics is a statistical science which has a profound influence on behaviours and resource allocation across the global academic ecosystem. It affects the allocation of energies and resources by researchers, authors, journals, publishers, universities, corporations and governments. Bibliometrics is primarily a product of two major information systems: The Web of Science, from Clarivate Analytics, and SCOPUS from Elsevier BV of the Netherlands. Both products are hugely complex systems. They must be managed and organised in an ordered and structured manner to be effective, through policies which describe the rules of content accrual, processing and delivery to its customers. Trust and Quality Assurance are central to the societal and commercial value of bibliometric systems. The designers of SCOPUS, with which I am most familiar, determined at the outset in 2003-2004 that content accrual would be managed through an external board of advisors, the SCOPUS Content Selection Advisory Board (CSAB). I have been privileged to be a member of the CSAB as the Subject Chair for Medicine since the outset of the current CSAB programme in 2009. This role has engaged me in the development of the SCOPUS Title Evaluation Platform (STEP); the major expansion and diversification of SCOPUS content; the diversification of bibliometrics; the growth of open access publishing; the move from subscription based to article processing fee based commerce; and the massive growth of sophisticated publication fraud; and the emergence of Machine Learning and Artificial Intelligence systems. In this essay, I seek to describe the work of the Board in terms of the development of its policy framework over the formative period 2010-2011.

Artificial Intelligence-Assisted Community Eye Screening in Primary Healthcare: A Prospective Multicentre Diagnostic Accuracy and Implementation Study

Background Artificial intelligence (AI)-assisted retinal screening may extend access to eye care in primary healthcare, but prospective evidence on diagnostic performance and implementation under routine community conditions remains limited. Methods We conducted a prospective multicentre diagnostic accuracy and implementation study across 12 urban and rural community eye-screening centres in India from 1 January to 30 June 2025. Adults aged ≥18 years underwent AI-assisted retinal-image analysis followed by masked comprehensive ophthalmic examination. The primary outcome was participant-level diagnostic accuracy of the AI-generated referral classification for the composite reference-standard outcome of any referable ocular disease. Implementation outcomes included image-acquisition success, workflow completion, referral compliance, screening time, and questionnaire-based acceptance and satisfaction. Results Among 1,732 participants, 610 (35.2%) had referable ocular disease. The AI system produced 566 true-positive, 1,017 true-negative, 105 false-positive, and 44 false-negative classifications. Sensitivity was 92.8% (95% CI 90.4%–94.7%), specificity 90.6% (88.8%–92.3%), positive predictive value 84.4% (81.4%–87.0%), negative predictive value 95.9% (94.5%–97.0%), and overall accuracy 91.4% (90.0%–92.7%). The F1 score was 0.884, Cohen’s kappa was 0.816, and the AUC based on the three-level AI risk classification was 0.925 (bootstrap 95% CI 0.912–0.937). Image acquisition succeeded in 1,668 participants (96.3%), workflow completion was 98.7%, and referral compliance was 550/671 (82.0%). Mean community-acceptance, healthcare-provider-satisfaction, and participant-satisfaction scores were 4.56, 4.44, and 4.49, respectively. Conclusions AI-assisted community eye screening showed high sensitivity and good overall diagnostic performance with strong operational feasibility. The false-positive burden, particularly among participants with diabetes, supports continued clinical oversight, image-quality assurance, and subgroup-specific validation before wider health-system adoption.

Making Scents Out of the Best "Smell" Tests in the Age of Clinical Artificial Intelligence.