Bibliographic record
Abstract
This paper analyzes mergers involving several leaders and followers in Stackelberg models, with the merged entity acting as a leader. Adding a follower to a merger increases its profitability or reduces its losses. A merger between one leader and any number of followers is always profitable. When a merger involves two leaders, it requires a sufficiently large proportion of followers to participate in it to be profitable. A merger is less likely to be profitable when the number of participating leaders is intermediate and the number of participating followers is small. All mergers involving leaders and followers are welfare reducing. Overall, Stackelberg leadership partially alleviates the merger paradox. / Ce papier analyse les fusions impliquant plusieurs meneurs et suiveurs dans les modèles de Stackelberg, où la firme fusionnée agit comme un meneur. Ajouter un suiveur à une fusion augmente sa profitabilité ou réduit ses pertes. Toute fusion entre un meneur et n’importe quel nombre de suiveurs est profitable. Lorsque deux meneurs participent à une fusion, il faut qu’un nombre suffisant de suiveurs s’y joignent pour qu’elle soit profitable. Une fusion a moins de chances d’être profitable lorsque le nombre de meneurs qui y participent est intermédiaire et le nombre de suiveurs est petit. Toutes les fusions impliquant des meneurs et des suiveurs réduisent le bien-être. En général, le leadership de Stackelberg allège en partie le paradoxe des fusions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".