The New Industrial Organization: Ecosystem Competition
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
This paper characterizes a new industrial organization framework for analyzing ecosystem formation and competition by recognizing the Schumpeterian force of creative destruction. Economists’ framework of profit maximization is replaced by a Welfare Enhancing framework (WEF)as a more pragmatic and realistic characterization of reality. Consumers are not fish in the ocean waiting to be preyed upon; they have free choice and broad lifestyle choices. The supply and demand framework is still relevant even though profit maximization in the theoretical sense that it has been technically crafted by economists may not. Firms as epistemic communities are more fitting as the behavioral assumption that can be more pragmatically applied. By using graphs and examples, three types of ecosystems are discussed, each sharing the commonality of data management as a driver for its respective ecosystem. The first two types of data management, coupled with pricing, bundling, and various industrial organization conducts, help to promote the welfare-enhancing growth of their respective ecosystems in an innocuous manner. The third type has an electrifying component resembling features of “two-sided” markets that may require Antitrust regulation. The key difference between the third and the first two types of competition is that the third type could lock in data with a specific investment of productivity less than the ideal optimal, thus reducing welfare rather than enhancing welfare.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it