An Attention Economy Theme Park
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 todayÂs 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it