Abstract: This foundational paper establishes Extended Career Self Theory (ECST), a sociomaterial framework designed to conceptualize and measure human agency within human–artificial intelligence assemblages (HAIA). As AI integration becomes ubiquitous, traditional models of career adaptability are insufficient for capturing the metacognitive demands placed on the human agent. Keywords: Career Sovereignty, Extended Career Self Theory (ECST), human–artificial intelligence assemblage, Assemblage, sociomateriality, authorship, agency, Career Self-Authorship, Algorithmic Alienation, Vocational Psychology, AI Ethics, Career Systemic Authorship (CSA), Sovereignty Gate, Sociotechnical Systems, SGf, SGₑ, Sᵣ Core Theoretical Architecture Summary: Career Systemic Authorship (CSA): The agent’s trait-level organismic baseline metacognitive capacity. Career Systemic Authorship Enactment (CSAe): The moment‑to‑moment expression of authorship within an HAIA. The Felt Sovereignty Gate (SGf): Monitors authentic authorial engagement. (SGf = State Authenticity / Trait Authenticity) The Enacted Sovereignty Gate (SGₑ): Quantifies the 'survival rate' of human agency in sociomaterial environments. (SGₑ = CSAe / CSA) The Career Sovereignty Ratio (Sᵣ): Monitors congruence between internal psychological states and external structural reality. (Sᵣ = SGf / SGₑ) Systemic Authorial Adaptability (SAA): Immediate tactical adjustments to sustain authorship. Systemic Career Resilience (SCR): Trait‑anchored longitudinal capacity to maintain sovereignty. Existential Anxiety: An exo‑outcome reflecting meaning‑threat and agency‑erosion. Human–Artificial Intelligence Assemblage (HAIA): The functional unit where Career Sovereignty is measured. Intellectual Property & Licensing: This paper establishes the original architecture for Extended Career Self Theory (ECST). All content defined in this paper is the original intellectual property of Lawrence P. W. Wong, © 2026. Distributed under CC BY-NC-ND 4. 0. This work is registered with a permanent DOI via Zenodo to establish prior art and theoretical provenance. Any derivative use, empirical operationalization, paraphrasing, unauthorized translation or publication, or adaptation of the underlying logic and mathematical relationships governing the concepts and architecture outlined in this paper—including unauthorized distribution, physical or digital reproduction (such as but not limited to photocopying, scanning, or screenshots), hosting on unauthorized platforms, formulaic substitution, variable relabelling, automated data mining, inclusion in AI training datasets or retrieval-augmented generation (RAG) systems, commercial weight-tuning of generative AI models, use in commercial training, or reverse engineering of the theoretical framework—requires express written permission and a separate licensing agreement from the author. Formal attribution is required for all non-commercial academic use. DOI: https: //doi. org/10. 5281/zenodo. 18758236
Lawrence P. W. Wong (Wed,) studied this question.