Bibliographic record
Abstract
Taxi industries across the world have been affected by a new trend in transportation; ridesharing services. It is suggested that this effect has been demonstrated through falling taxi medallion prices. This recent decline in taxi medallion prices has been coined the term “The Uber Effect”. This paper analyzes the effect that Uber has had on the taxi industry’s medallion prices since UberX has entered three different markets: New York City, Chicago and Philadelphia. The price of a taxi medallion is modeled against a variable of interest: number of Uber drivers in a city, and control variables: unemployment rate, long term interest rate and labor force population. Through individual city and panel regression analysis, The Uber Effect is tested and quantified.\nThe key finding from this paper is that the number of Uber drivers in the market is negatively correlated with the price of a taxi medallion, as expected. It is statistically and economically significant; each additional Uber driver reduces the price of a taxi medallion by $22 to $45. Furthermore, the unemployment rate and labor force population variables are statistically significant in all cities used in this study.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.005 |
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".