Learning Model Orchestration
Intentional selection, sequencing, and combination of learning models when information is abundant but learning architecture is scarce.
Lexicon
Short, precise entries for frameworks introduced in SSRN working papers and cover essays. Each entry defines a concept you can cite in essays, talks, and the Annual Review.
Judgment-as-Product
Under generative abundance, value completes through accountable human judgment — not at the moment of AI generation.
Orchestration Inequality
Unequal capacity to select, combine, and govern effective learning models — when the same AI tool yields different learning architectures.
Generative Abundance Paradox
More plausible AI outputs can expand evaluative work faster than they compress responsible decision time.
AI Waiting Tax
The hidden psychological and cognitive cost of waiting for AI responses — idle time as suspended attention, not free time.
AI Latency Tax
Latency as a structural, distributive externality in intelligent systems — invisible, cumulative, and unevenly borne.
Expression Distortion Cost
Performance loss when expressed input to AI diverges from the user's underlying goals, constraints, and preferences.
Self-Alignment Load
The cognitive effort required to identify, structure, and articulate internal states before prompting AI.
Authenticity–Cost Model
How expression distortion cost and self-alignment load jointly shape interaction quality in human–AI dialogue.
Motivational Reweighting
Generative AI reorganizes activation costs and institutional rewards of long-standing creative motives — it does not replace them.
Three Creative Motives
Expression-driven, feedback-driven, and outcome-driven creation — coexistent motives, not substitutive stages of history.
Suspended Attention
Waiting for AI as tethered partial engagement — neither rest nor full task focus.
Temporal Justice
Fairness in how waiting time and responsiveness are allocated when AI mediates essential services.
Judgment Burden
The hidden cognitive labor of evaluating, verifying, and standing behind AI outputs under generative abundance.
Co-Produced Misalignment
Interaction failure jointly produced by model capability and user-side representational fidelity — not model-driven alone.
Learning Model Scarcity
When information is abundant, the binding constraint shifts from knowledge access to the design and orchestration of learning models.
Expression-Weight Restoration
When generative friction falls, low-stakes personal expression regains practical weight — not as professional output, but as self-directed making.