Bibliographic record
Abstract
In chapter 1, I study how spillover effects from competitors' choices affect a firm's decision to open a store. Using panel data from the United Kingdom's fast food industry, I propose and estimate a game of entry under incomplete information that incorporates spillover effects between firms' entry decisions. A positive spillover is identified for Burger King - increasing the stock of existing McDonald's by 1 outlet increases Burger King's estimated equilibrium probability of opening a new store by approximately 18 percentage points. Chapter 2 advances our collective knowledge about the impact of learning from others in industry dynamics, and whether it can generate the clustering of rival retailers. Uncertainty about new markets provides an opportunity for learning from others, where one firm's past entry decisions signal to others the potential profitability of risky markets. The setting is Canada's hamburger fast food industry from its inception in 1970 to 2005, where I introduce a new estimable dynamic oligopoly model of entry/exit with unobserved heterogeneity, common uncertainty about demand, learning through entry, and learning from others. I find that the presence of uncertainty induces retailers to herd into markets that others have previously done well in. Finally, chapter 3 (joint with Feng Chi) studies the early adoption of Twitter in the 111th House of Representatives. Our main objective is to determine whether successes of past adopters have the tendency to speed up Twitter adoption, where past success is defined as the average followers per Tweet - a common measure of "Twitter success" - among all prior adopters. The data suggests that accelerated adoption can be associated with favorable past outcomes: increasing the average number of followers per Tweet among past adopters by a standard deviation (of 8 followers per Tweet) accelerates the adoption time by about 112 days.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".