Before Brooklyn: The Unsung Heroes Who Helped Break Baseball’s Color Barrier by Ted Reinstein
Bibliographic record
Abstract
Reviewed by: Before Brooklyn: The Unsung Heroes Who Helped Break Baseball’s Color Barrier by Ted Reinstein Chad Wise Ted Reinstein. Before Brooklyn: The Unsung Heroes Who Helped Break Baseball’s Color Barrier. Guilford, CT: Lyons Press, 2021. 254 pp. Cloth, $29.95. The year 2022 marks seventy-five years since Jackie Robinson broke the color barrier and played Major League Baseball (MLB) for the Brooklyn Dodgers. Today, on April 15 of each season, professional players and coaches wear Robinson’s famous jersey, number 42. No other major league ballplayer will ever wear the number 42 again. The story is as American as apple pie. However, in Ted Reinstein’s book Before Brooklyn: The Unsung Heroes Who Helped Break Baseball’s Color Barrier, readers learn that, despite Robinson’s heroic (and dangerous) act, the color barrier had already been broken nearly thirty-five years before Robinson was even born. Throughout the book, Reinstein shares story after story of baseball players who had everything going for them, except their skin color. As far back as the Civil War, Union prisoners of war were taught to play baseball by their southern counterparts in camps, but Black soldiers could not participate. A Black person could fight and die for the war cause but couldn’t play third base on a “white” field. Fast-forward to the Jim Crow era, and things were not much better for Black baseball players, or for Blacks in general. Making a farce out of the “separate but equal” doctrine, according to Reinstein, “an Ohio Wesleyan player was refused a room at the team’s hotel. The team’s white manager asked for, and received, special permission to allow the catcher to sleep on a cot in his room. So long as the relationship of master and servant was obvious” (49). Reinstein shares accounts of the era, when teams sent letters threatening violence to opposing teams if a Black player even showed up to the field, let alone played. From 1915 to 1960, the Great Migration caused a seismic shift in populations in the US. Rampant racism, a deepening hopelessness of segregation, and the lack of economic opportunities moved African Americans to cities such as Chicago, Detroit, Cleveland, Philadelphia, and New York. Reinstein shares [End Page 117] that the Great Migration played an important role in the development of baseball in the northern states: “More than five million American Blacks uprooted from the South and moved northward” (55). The burgeoning Black “metropolis” of the Northeast and Midwest facilitated the establishment of separate hospitals, banks, self-help organizations, publications, and businesses, most notably the continued expansion and development of “black baseball” (55). It was clear that African American players would not get a fair opportunity to play on or against white teams. Luckily, a man named Andrew “Rube” Foster had a plan to bring African American players together. As a former player, manager, and now entrepreneur, Foster proposed a new league in the early stages of the Great Migration. His famous quote—“We are the ship, all else the sea” (58)—was a rallying cry for the beginning of a new league, the Negro National League. When looking at important cities in America during the Great Migration, Pittsburgh stands out among so many others. Its location brought people together coming from the northern and eastern parts of the South. Pittsburgh offered educational opportunities for African Americans, including integrated public schools. In addition, sport became something that began bringing the African American community together. No sport brought people together in the 1930s like baseball. Even the local newspapers got involved in the hoopla surrounding baseball in Pittsburgh. Many believe that if newspapers such as the Pittsburgh Courier hadn’t reported on the teams and players, “few people would even know about Black baseball” (86). Two players stood out while playing in Pittsburgh and are considered pioneers of baseball: Josh Gibson and Satchel Paige. Their numbers could be considered “video game numbers” by today’s standards. But what if the hometown major league team, the Pirates, were to add an arm, glove, and bat from Gibson or Paige to their lineup? History can only speculate how these additions might have played out...
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.062 |
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".