Sustainable digital rent: a transformative framework for value dynamics in the digital age
Bibliographic record
Abstract
Introduction This paper introduces the concept of Sustainable Digital Rent (SDR), highlighting the shift from traditional economic rent based on tangible assets to rent derived from digital platforms. At the heart of this shift is the “value state,” a dynamic balance between constructive expectations and destructive information. As digital platforms generate increasing amounts of information, expectations are increasingly met and shared more efficiently with all users, leading to a reduction in individual and general motivational, emotional, and cognitive engagement. These platforms, now essential to modern life, facilitate online activities that reduce as well physical engagement and natural interactions, thereby impacting cognitive function and physical health. By extracting rent directly, digital platform operators limit the benefits users could gain to support their mental and physical well-being. Methods This paper empirically defines and estimates SDR using the collective estimates of price, cost, and income (PCI) as practiced in North American real estate appraisal, demonstrated through abstract art rent. Our approach provides a new perspective on valuing intangible assets, such as knowledge, by showing the shift from expectation to information, governed by the value state in cognitive evaluations. Emphasizing interdisciplinary relevance, the method underscores the need for an efficient mechanism to redistribute SDR benefits to digital platform users, supporting fair and equitable digital development. Results and discussion The results show that digital rent is driven primarily by cognitive and informational content, demonstrating the need for redistribution mechanisms to address the growing inequality on digital platforms. The use of abstract art as a case study provides a convenient and illustrative way to explore how intangible assets, like digital rents, can be evaluated and redistributed. SDR offers insights into how digital rents can be captured and redistributed equitably, ensuring that platform users and creators benefit from the knowledge economy’s growth. The findings underscore the relevance of measuring SDR to guide policy recommendations aimed at reducing digital monopolization and promoting sustainable digital development.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".