Bibliographic record
Abstract
Patent holdup can arise when circumstances enable a patent owner to extract a larger royalty ex post than it could have obtained in an arms length transaction ex ante.While the concept of patent holdup is familiar to scholars and practitioners-particularly in the context of standard-essential patent (SEP) disputes-the economic details are frequently misunderstood.For example, the popular assumption that switching costs (those required to switch from the infringing technology to an alternative) necessarily contribute to holdup is false in general, and will tend to overstate the potential for extracting excessive royalties.On the other hand, some commentaries mistakenly presume that large fixed costs are an essential ingredient of patent holdup, which understates the scope of the problem.In this Article, we clarify and distinguish the most basic economic factors that contribute to patent holdup.This casts light on various points of confusion arising in many commentaries on the subject.Path dependence-which can act to inflate the value of a
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".