Bibliographic record
Abstract
Technology lock-in advocates argue that governments should step in to coordinate technology adoption decisions. Due to the presence of network effects, advocates warn that consumers may fail to adopt the best technology, thus missing out on potential benefits. Even worse, consumers may split, adopting multiple technologies and thus missing out on the benefits of network effects. Due to coordination problems, consumers cannot mitigate the effects of bad technology choices and the economy becomes stuck with inferior innovations. This article demonstrates that consumer coordination solves the underlying network effects problem, thus eliminating technology lock-in. Network effects are confined at most to the information and communications technology and selected electronics industries, which have developed mechanisms for interconnection and interoperability. Firms have incentives to provide interconnection and interoperability when it is efficient to do so. Rapid technological innovation is apparent whereas technology lock-in is a rare phenomenon. Antitrust policy founded on technology lock-in arguments is misguided and is likely to damage incentives for innovation.
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.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".