Bibliographic record
Abstract
Startup acquisitions by dominant incumbents, especially in high tech, have recently attracted significant attention. Many researchers and practitioners worry about harms to competition or innovation. However, there has been very little antitrust enforcement in this area. This is emblematic of a prominent feature of modern antitrust law: a strong preference for erring on the side of nonenforcement. A leading rationale for this preference is the claim that market power self-corrects by attracting new entrants who discipline incumbents.\nAs a result, plaintiffs generally face very demanding evidentiary requirements, which are particularly hard to satisfy in the case of startup acquisitions. A typical startup is both new and small, providing little data for estimating competitive effects. Despite this uncertainty, it is unlikely that society is best served by a policy of near-universal inaction. Recent work in economics, both empirical and theoretical, identifies various harms to competition and innovation as a result of startup acquisitions in concentrated markets. Further, the traditional error cost argument is particularly inapposite in this context, as startup acquisitions may be undertaken precisely because they forestall competitive entry. We therefore argue for expanded antitrust intervention (that is, more than zero) in startup acquisitions by dominant incumbents. In practice, the acquirer’s market power and the transaction value may be useful signals of the risk of harm
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".