An Attention Economy Theme Park
Bibliographic record
Abstract
Planetary scale computation is evolving the way we digitize the physical urban space. The following research aims to provide an architectural response to the accelerating digitization of our physical world and societal life processes of economy and communications. It acknowledges the legitimate bias in the perceptual value of territories favored by the new Attention Economy of the Metaverse and Blockchain-based Virtual Environments. It proposes the analogy of a theme park derived from the distorted collective vision of today’s reality, of a reduced collection of favored attraction locations. The research provides first a review of contemporary studies related to the operation of the Attention Economy in the Metaverse, Web3 platforms, and Gamified Virtual Environments, as well as studies on recent architectural expressions or typologies of these spaces. A series of methodologies are described next to convey the impact of recent advances in Artificial Intelligence (AI) on the creation of digital personas and worldmaking for this type of economy. The methodologies comprise a three-stage workflow based on data mining and curation, processing through AI-aided generative methods, and implementation with game engine environments, ultimately discussed regarding simulation and creative agency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".