MLB Expansion: Analyzing the cities, factors, implementation, and draft process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
abstract: The purpose of this thesis is to cover the multiple aspects of Major League Baseball Expansion from 30 to 32 teams. The thesis can be divided into two parts with the first being the preparation and consideration for expansion, and the second half is about the execution and implementation of adding two expansion teams to the league. \n\tFor years, Commissioner Rob Manfred has hinted and brought about the idea of adding two more teams to Major League Baseball (Mitchell). The growth of the game is of utmost importance, and they have made many changes to try to expand the growth of fans the past few years particularly catered to new and young fans. New rules like a pitch clock and mound visit limitations are examples of in game changes made to speed up the game, but they have also experimented with spring training and regular season games internationally or at new venues. In just the past decade, games have been played or planned (due to COVID-19 cancellations) in Monterrey, Mexico City, London, Tokyo, San Juan, Montreal, Las Vegas, Williamsport, and even Iowa. With the exception of the Williamsport Little League Classic and the Field of Dreams game in Iowa, all these locations had games to see what the atmosphere and logistics would be like with expansion in mind as a possibility in the future. With this in mind, this thesis will analyze and come to a conclusion on the following cities for the best fits for expansion: Monterrey, Mexico City, San Juan, Vancouver, Montreal, Las Vegas, Portland, Nashville, Raleigh, and San Antonio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it